Èric Pairet | University of Edinburgh (original) (raw)

Papers by Èric Pairet

Research paper thumbnail of Learning and Generalisation of Primitives Skills Towards Robust Dual-arm Manipulation

Robots are becoming a vital ingredient in society. Some of their daily tasks require dual-arm man... more Robots are becoming a vital ingredient in society. Some of their daily tasks require dual-arm manipulation skills in the rapidly changing, dynamic and unpredictable real-world environments where they have to operate. Given the expertise of humans in conducting these activities, it is natural to study humans' motions to use the resulting knowledge in robotic control. With this in mind, this work leverages human knowledge to formulate a more general, real-time, and less task-specific framework for dual-arm manipulation. The proposed framework is evaluated on the iCub humanoid robot and several synthetic experiments, by conducting a dual-arm pick-and-place task of a parcel in the presence of unexpected obstacles. Results suggest the suitability of the method towards robust and generalisable dual-arm manipulation.

Research paper thumbnail of Towards Robust Grasps: Using the Environment Semantics for Robotic Object Affordances

Artificial Intelligence is essential to achieve a reliable human-robot interaction, especially wh... more Artificial Intelligence is essential to achieve a reliable human-robot interaction, especially when it comes to manipulation tasks. Most of the state-of-the-art literature explores robotics grasping methods by focusing on the target object or the robot's morphology, without including the environment. When it comes to human cognitive development approaches, these physical qualities are not only inferred from the object, but also from the semantic characteristics of the surroundings. The same analogy can be used in robotic affordances for improving objects grasps, where the perceived physical qualities of the objects give valuable information about the possible manipulation actions. This work proposes a framework able to reason on the object affordances and grasping regions. Each calculated grasping area is the result of a sequence of concrete ranked decisions based on the inference of different highly related attributes. The results show that the system is able to infer on good grasping areas depending on its affordance without having any a-priori knowledge on the shape nor the grasping points.

Research paper thumbnail of Uncertainty-based Online Mapping and Motion Planning for Marine Robotics Guidance

In real-world robotics, motion planning remains to be an open challenge. Not only robotic systems... more In real-world robotics, motion planning remains to be an open challenge. Not only robotic systems are required to move through unexplored environments, but also their manoeuvrability is constrained by their dynamics and often suffer from uncertainty. One approach to overcome this problem is to incrementally map the surroundings while, simultaneously, planning a safe and feasible path to a desired goal. This is especially critical in underwater environments, where autonomous vehicles must deal with both motion and environment uncertainties. In order to cope with these constraints, this work proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety-guarantees. The proposed approach deals with the motion, probabilistic safety, and online computation constraints by (i) incrementally representing the environment as a collection of local maps, and (ii) iteratively (re)planning kinodynamically-feasible and probabilistically-safe paths to goal. The proposed framework is evaluated on the Sparus II, a nonholonomic torpedo-shaped AUV, by conducting simulated and real-world trials, thus proving the efficacy of the method and its suitability even for systems with limited on-board computational power.

Research paper thumbnail of Simultaneous Mapping and Planning for Autonomous Underwater Vehicles in Unknown Environments

New potential applications of autonomous underwater vehicles (AUVs) involve operations in unknown... more New potential applications of autonomous underwater vehicles (AUVs) involve operations in unknown and cluttered environments, therefore increasing the vehicle exposure to collisions. To cope with these situations, we use an AUV framework for planning collision-free paths in unknown environments, which adapt and replan the paths according to nearby obstacles perceived during the mission execution using different range sensing sonar. We present simulation and real-world results for the SPARUS-II AUV, a torpedo-shaped vehicle, performing autonomous missions.

Research paper thumbnail of Simultaneous mapping and planning for autonomous underwater vehicles in unknown environments

OCEANS 2015 - Genova, 2015

Research paper thumbnail of Testing SPARUS II AUV, an open platform for industrial, scientific and academic applications

This paper describes the experience of preparing and testing the SPARUS II AUV in different appli... more This paper describes the experience of preparing and testing the SPARUS II AUV in different applications. The AUV was designed as a lightweight vehicle combining the classical torpedo-shape features with the hovering capability. The robot has a payload area to allow the integration of different equipment depending on the application. The software architecture is based on ROS, an open framework that allows an easy integration of many devices and systems. Its flexibility, easy operation and openness makes the SPARUS II AUV a multipurpose platform that can adapt to industrial, scientific and academic applications. Five units were developed in 2014, and different teams used and adapted the platform for different applications. The paper describes some of the experiences in preparing and testing this open platform to different applications.

Research paper thumbnail of On-line 3D Path Planning for Close-proximity Surveying with AUVs

We present an approach for planning collision-free paths on-line for an underwater multi-robot sy... more We present an approach for planning collision-free paths on-line for an underwater multi-robot system, which is composed by a leading Autonomous Underwater Vehicle (AUV) endowed with a multibeam sonar and high processing capabilities and a second AUV. While the leading AUV follows a safe, pre-planned survey path, the second vehicle, herein referred to as Camera Vehicle (CV), must survey the bottom in close proximity while following the leader, complementing its survey capabilities. Due to their proximity to the bottom, the CV is exposed to a collision threat. We address this problem by incrementally building a 3D map of the environment onboard the leading vehicle by means of its multibeam sonar. Using this map, we plan on-line 3D paths that are transferred to the CV for close and safe surveying of the bottom. These paths are planned using the Transition-based RRT (T-RRT) algorithm, which is an RRT-variant that considers a cost function defined over the vehicle's configuration space, or costmap for short. By defining a costmap in terms of distance to the bottom and path distance, we are able to keep the paths at a desired offset distance from the bottom for constant-resolution surveying. We have integrated our path planning system with the software architecture of the SPARUS-II and GIRONA500 AUVs. We demonstrate the feasibility of our approach in simulation. The multi-robot system presented is based on the context of the MORPH FP7 EU project.

Research paper thumbnail of On-line 3D Path Planning for Close-proximity Surveying with AUVs★

IFAC-PapersOnLine, 2015

We present an approach for planning collision-free paths on-line for an underwater multi-robot sy... more We present an approach for planning collision-free paths on-line for an underwater multi-robot system, which is composed by a leading Autonomous Underwater Vehicle (AUV) endowed with a multibeam sonar and high processing capabilities and a second AUV. While the leading AUV follows a safe, pre-planned survey path, the second vehicle, herein referred to as Camera Vehicle (CV), must survey the bottom in close proximity while following the leader, complementing its survey capabilities. Due to their proximity to the bottom, the CV is exposed to a collision threat. We address this problem by incrementally building a 3D map of the environment onboard the leading vehicle by means of its multibeam sonar. Using this map, we plan on-line 3D paths that are transferred to the CV for close and safe surveying of the bottom. These paths are planned using the Transition-based RRT (T-RRT) algorithm, which is an RRT-variant that considers a cost function defined over the vehicle's configuration space, or costmap for short. By defining a costmap in terms of distance to the bottom and path distance, we are able to keep the paths at a desired offset distance from the bottom for constant-resolution surveying. We have integrated our path planning system with the software architecture of the SPARUS-II and GIRONA500 AUVs. We demonstrate the feasibility of our approach in simulation. The multi-robot system presented is based on the context of the MORPH FP7 EU project.

Thesis by Èric Pairet

Research paper thumbnail of Learning and Generalisation of Primitive Skills for Robust Dual-arm Manipulation

Robots are becoming a vital ingredient in society. Some of their daily tasks require dual-arm man... more Robots are becoming a vital ingredient in society. Some of their daily tasks require dual-arm manipulation skills in the rapidly changing, dynamic and unpredictable real-world environments where they have to operate. Given the expertise of humans in conducting these activities, it is natural to study humans motions to use the resulting knowledge in robotic control. With this in mind, this work leverages human knowledge to formulate a more general, real-time, and less task-specific framework for dual-arm manipulation. Particularly, the proposed architecture first learns the dynamics underlying the execution of different primitive skills. These are harvested in a one-at-a-time fashion from human demonstrations, making dual-arm systems accessible to non-roboticists-experts. Then, the framework exploits such knowledge simultaneously and sequentially to confront complex and novel scenarios.
Current works in the literature deal with the challenges arising from particular dual-arm appli- cations in controlled environments. Thus, the novelty of this work lies in (i) learning a set of primitive skills in a one-at-a-time fashion, and (ii) endowing dual-arm systems with the abil- ity to reuse their knowledge according to the requirements of any commanded task, as well as the surrounding environment. The potential of the proposed framework is demonstrated with several experiments involving synthetic environments, the simulated and real iCub humanoid robot. Apart from evaluating the performance and generalisation capabilities of the different primitive skills, the framework as a whole is tested with a dual-arm pick-and-place task of a parcel in the presence of unexpected obstacles. Results suggest the suitability of the method towards robust and generalisable dual-arm manipulation.

Research paper thumbnail of Uncertainty-based online mapping and motion planning for marine robotics guidance

In real-world robotics, path planning remains to be an open challenge; not only robots are asked ... more In real-world robotics, path planning remains to be an open challenge; not only robots are asked to move through unexplored environments, but also the motion of robots is constrained by their dynamics. At the same time, such dynamics typically suffer from uncertainties, which should be taken into account for completely ensuring the feasibility of the path and the robot’s safety.
The state-of-the-art usually addresses those issues separately. Planning online requires being able to quickly update the path according to the incremental knowledge of the environment. Such prescription is hard to be satisfied when considering the system dynamics and its uncertainty because a policy over the entire belief space must be constructed.
This work proposes an incremental mapping-planning framework that jointly addresses these challenges for achieving fast replanning. The framework is threefold: (1) the environment is represented as a collection of local maps, for each of which the system has a relative uncertainty so (2) the probability of colliding with the environment can be probabilistically checked and (3) the feasibility of the path is ensured by considering the kinodynamic constraints of the system.
The proposed framework is evaluated with the Sparus II AUV, a torpedo-shaped vehicle suf- fering from nonholonomic constraints. The experiments are conducted in simulated and real-world environments, such as a breakwater structure and a natural passage. Results show the potential of the method for planning under motion and probabilistic constraints in uncertain environments while being suitable for systems with limited computational power.

Research paper thumbnail of Integració de timons en el robot SPARUS II pel control en cinc graus de llibertat

Research paper thumbnail of Learning and Generalisation of Primitives Skills Towards Robust Dual-arm Manipulation

Robots are becoming a vital ingredient in society. Some of their daily tasks require dual-arm man... more Robots are becoming a vital ingredient in society. Some of their daily tasks require dual-arm manipulation skills in the rapidly changing, dynamic and unpredictable real-world environments where they have to operate. Given the expertise of humans in conducting these activities, it is natural to study humans' motions to use the resulting knowledge in robotic control. With this in mind, this work leverages human knowledge to formulate a more general, real-time, and less task-specific framework for dual-arm manipulation. The proposed framework is evaluated on the iCub humanoid robot and several synthetic experiments, by conducting a dual-arm pick-and-place task of a parcel in the presence of unexpected obstacles. Results suggest the suitability of the method towards robust and generalisable dual-arm manipulation.

Research paper thumbnail of Towards Robust Grasps: Using the Environment Semantics for Robotic Object Affordances

Artificial Intelligence is essential to achieve a reliable human-robot interaction, especially wh... more Artificial Intelligence is essential to achieve a reliable human-robot interaction, especially when it comes to manipulation tasks. Most of the state-of-the-art literature explores robotics grasping methods by focusing on the target object or the robot's morphology, without including the environment. When it comes to human cognitive development approaches, these physical qualities are not only inferred from the object, but also from the semantic characteristics of the surroundings. The same analogy can be used in robotic affordances for improving objects grasps, where the perceived physical qualities of the objects give valuable information about the possible manipulation actions. This work proposes a framework able to reason on the object affordances and grasping regions. Each calculated grasping area is the result of a sequence of concrete ranked decisions based on the inference of different highly related attributes. The results show that the system is able to infer on good grasping areas depending on its affordance without having any a-priori knowledge on the shape nor the grasping points.

Research paper thumbnail of Uncertainty-based Online Mapping and Motion Planning for Marine Robotics Guidance

In real-world robotics, motion planning remains to be an open challenge. Not only robotic systems... more In real-world robotics, motion planning remains to be an open challenge. Not only robotic systems are required to move through unexplored environments, but also their manoeuvrability is constrained by their dynamics and often suffer from uncertainty. One approach to overcome this problem is to incrementally map the surroundings while, simultaneously, planning a safe and feasible path to a desired goal. This is especially critical in underwater environments, where autonomous vehicles must deal with both motion and environment uncertainties. In order to cope with these constraints, this work proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety-guarantees. The proposed approach deals with the motion, probabilistic safety, and online computation constraints by (i) incrementally representing the environment as a collection of local maps, and (ii) iteratively (re)planning kinodynamically-feasible and probabilistically-safe paths to goal. The proposed framework is evaluated on the Sparus II, a nonholonomic torpedo-shaped AUV, by conducting simulated and real-world trials, thus proving the efficacy of the method and its suitability even for systems with limited on-board computational power.

Research paper thumbnail of Simultaneous Mapping and Planning for Autonomous Underwater Vehicles in Unknown Environments

New potential applications of autonomous underwater vehicles (AUVs) involve operations in unknown... more New potential applications of autonomous underwater vehicles (AUVs) involve operations in unknown and cluttered environments, therefore increasing the vehicle exposure to collisions. To cope with these situations, we use an AUV framework for planning collision-free paths in unknown environments, which adapt and replan the paths according to nearby obstacles perceived during the mission execution using different range sensing sonar. We present simulation and real-world results for the SPARUS-II AUV, a torpedo-shaped vehicle, performing autonomous missions.

Research paper thumbnail of Simultaneous mapping and planning for autonomous underwater vehicles in unknown environments

OCEANS 2015 - Genova, 2015

Research paper thumbnail of Testing SPARUS II AUV, an open platform for industrial, scientific and academic applications

This paper describes the experience of preparing and testing the SPARUS II AUV in different appli... more This paper describes the experience of preparing and testing the SPARUS II AUV in different applications. The AUV was designed as a lightweight vehicle combining the classical torpedo-shape features with the hovering capability. The robot has a payload area to allow the integration of different equipment depending on the application. The software architecture is based on ROS, an open framework that allows an easy integration of many devices and systems. Its flexibility, easy operation and openness makes the SPARUS II AUV a multipurpose platform that can adapt to industrial, scientific and academic applications. Five units were developed in 2014, and different teams used and adapted the platform for different applications. The paper describes some of the experiences in preparing and testing this open platform to different applications.

Research paper thumbnail of On-line 3D Path Planning for Close-proximity Surveying with AUVs

We present an approach for planning collision-free paths on-line for an underwater multi-robot sy... more We present an approach for planning collision-free paths on-line for an underwater multi-robot system, which is composed by a leading Autonomous Underwater Vehicle (AUV) endowed with a multibeam sonar and high processing capabilities and a second AUV. While the leading AUV follows a safe, pre-planned survey path, the second vehicle, herein referred to as Camera Vehicle (CV), must survey the bottom in close proximity while following the leader, complementing its survey capabilities. Due to their proximity to the bottom, the CV is exposed to a collision threat. We address this problem by incrementally building a 3D map of the environment onboard the leading vehicle by means of its multibeam sonar. Using this map, we plan on-line 3D paths that are transferred to the CV for close and safe surveying of the bottom. These paths are planned using the Transition-based RRT (T-RRT) algorithm, which is an RRT-variant that considers a cost function defined over the vehicle's configuration space, or costmap for short. By defining a costmap in terms of distance to the bottom and path distance, we are able to keep the paths at a desired offset distance from the bottom for constant-resolution surveying. We have integrated our path planning system with the software architecture of the SPARUS-II and GIRONA500 AUVs. We demonstrate the feasibility of our approach in simulation. The multi-robot system presented is based on the context of the MORPH FP7 EU project.

Research paper thumbnail of On-line 3D Path Planning for Close-proximity Surveying with AUVs★

IFAC-PapersOnLine, 2015

We present an approach for planning collision-free paths on-line for an underwater multi-robot sy... more We present an approach for planning collision-free paths on-line for an underwater multi-robot system, which is composed by a leading Autonomous Underwater Vehicle (AUV) endowed with a multibeam sonar and high processing capabilities and a second AUV. While the leading AUV follows a safe, pre-planned survey path, the second vehicle, herein referred to as Camera Vehicle (CV), must survey the bottom in close proximity while following the leader, complementing its survey capabilities. Due to their proximity to the bottom, the CV is exposed to a collision threat. We address this problem by incrementally building a 3D map of the environment onboard the leading vehicle by means of its multibeam sonar. Using this map, we plan on-line 3D paths that are transferred to the CV for close and safe surveying of the bottom. These paths are planned using the Transition-based RRT (T-RRT) algorithm, which is an RRT-variant that considers a cost function defined over the vehicle's configuration space, or costmap for short. By defining a costmap in terms of distance to the bottom and path distance, we are able to keep the paths at a desired offset distance from the bottom for constant-resolution surveying. We have integrated our path planning system with the software architecture of the SPARUS-II and GIRONA500 AUVs. We demonstrate the feasibility of our approach in simulation. The multi-robot system presented is based on the context of the MORPH FP7 EU project.

Research paper thumbnail of Learning and Generalisation of Primitive Skills for Robust Dual-arm Manipulation

Robots are becoming a vital ingredient in society. Some of their daily tasks require dual-arm man... more Robots are becoming a vital ingredient in society. Some of their daily tasks require dual-arm manipulation skills in the rapidly changing, dynamic and unpredictable real-world environments where they have to operate. Given the expertise of humans in conducting these activities, it is natural to study humans motions to use the resulting knowledge in robotic control. With this in mind, this work leverages human knowledge to formulate a more general, real-time, and less task-specific framework for dual-arm manipulation. Particularly, the proposed architecture first learns the dynamics underlying the execution of different primitive skills. These are harvested in a one-at-a-time fashion from human demonstrations, making dual-arm systems accessible to non-roboticists-experts. Then, the framework exploits such knowledge simultaneously and sequentially to confront complex and novel scenarios.
Current works in the literature deal with the challenges arising from particular dual-arm appli- cations in controlled environments. Thus, the novelty of this work lies in (i) learning a set of primitive skills in a one-at-a-time fashion, and (ii) endowing dual-arm systems with the abil- ity to reuse their knowledge according to the requirements of any commanded task, as well as the surrounding environment. The potential of the proposed framework is demonstrated with several experiments involving synthetic environments, the simulated and real iCub humanoid robot. Apart from evaluating the performance and generalisation capabilities of the different primitive skills, the framework as a whole is tested with a dual-arm pick-and-place task of a parcel in the presence of unexpected obstacles. Results suggest the suitability of the method towards robust and generalisable dual-arm manipulation.

Research paper thumbnail of Uncertainty-based online mapping and motion planning for marine robotics guidance

In real-world robotics, path planning remains to be an open challenge; not only robots are asked ... more In real-world robotics, path planning remains to be an open challenge; not only robots are asked to move through unexplored environments, but also the motion of robots is constrained by their dynamics. At the same time, such dynamics typically suffer from uncertainties, which should be taken into account for completely ensuring the feasibility of the path and the robot’s safety.
The state-of-the-art usually addresses those issues separately. Planning online requires being able to quickly update the path according to the incremental knowledge of the environment. Such prescription is hard to be satisfied when considering the system dynamics and its uncertainty because a policy over the entire belief space must be constructed.
This work proposes an incremental mapping-planning framework that jointly addresses these challenges for achieving fast replanning. The framework is threefold: (1) the environment is represented as a collection of local maps, for each of which the system has a relative uncertainty so (2) the probability of colliding with the environment can be probabilistically checked and (3) the feasibility of the path is ensured by considering the kinodynamic constraints of the system.
The proposed framework is evaluated with the Sparus II AUV, a torpedo-shaped vehicle suf- fering from nonholonomic constraints. The experiments are conducted in simulated and real-world environments, such as a breakwater structure and a natural passage. Results show the potential of the method for planning under motion and probabilistic constraints in uncertain environments while being suitable for systems with limited computational power.

Research paper thumbnail of Integració de timons en el robot SPARUS II pel control en cinc graus de llibertat