Shared Autonomy: Learning and Control
June 4th 2020 during ICRA 2020,
Palais de Congres, Paris, France
=> virtual meeting on zoom, starting at 8:30 AM CEST
Link ZOOM: https://oulu.zoom.us/j/68686148336
Password to enter the zoom: @SALC2020 (included the @)
=> On the ICRA2020 slack, look for the channel #ws22
See the Youtube playlist: https://www.youtube.com/playlist?list=PLaViAl2WLPMecZ8q58U5Nxi8vFwgMLKTs
Shared autonomy enables humans to effectively and comfortably realize complex tasks by physically or remotely interacting with robotic systems. Nowadays, research on shared autonomy systems is playing a relevant role in a wide spectrum of robotic applications ranging from surgical to industrial, from single arm systems to robot swarms. Recently, the availability of datasets and the advancement of machine learning techniques have enabled enhanced flexibility of shared autonomy systems that are now capable of providing contextual or personalized assistance and seamless adaption of the autonomy level. However, this desirable trend raises new challenges for safety and stability certification of shared autonomy robotic systems, thus requiring new advanced control methods to implement the continuously evolving division of roles.
This workshop brings together renowned scientists from the machine learning and control communities devoted to shared autonomy robotics research. The goal is to foster the discussion about the state-of-art in the research field from different perspectives in pursuit of coordinated solutions for building the next generation of shared autonomy robotic systems.
The workshop will consists of invited talks (30 min) and presentations from selected contributions after a call for papers/posters. The workshop will close with a panel discussion.
Program at a glance
|8:30 – 9:00||Introduction by the organizers
|9:00 – 9:30||Oussama Khatib
Human-Robot Collaboration: Interfaces and Control Architecture
|9:30 – 10:00||Leonel Rozo
Leveraging domain knowledge for efficient learning and adaptation of robotic skills
|10:00 – 10:30||Paolo Robuffo Giordano
|10:30 – 11:00||Coffee Break|
|11:00 – 11:30||Arash Ajoudani
Shared authority control of a MObile Collaborative robot Assistant (MOCA)
|11:30 – 12:00||Daniele Pucci
Wearable Technologies, Estimation, and Control for Human Robot Collaboration with an Application to COVID-19
|12:00 – 12:30|
|12:30 – 13:00||Poster / video teasers pt.1|
|13:00 – 14:00||Lunch Break|
|14:00 – 14:30||Poster / video teasers pt.2|
|14:30 – 15:00||Cristian Secchi
Shared control for human-robot interaction: an energy based perspective
|15:00 – 15:30||Paolo Rocco
Occlusion-free visual servoing for the shared autonomy teleoperation of dual-arm robots
|15:30 – 16:30||Coffee Break|
|16:30 – 17:00||Allison Okamura
Human Interface for Teleoperated Object Manipulation with a Soft Growing Robot
|17:00 – 17:30||Byron Boots
Online Learning for Adaptive Robotic Systems
|17:30 – 18:00||Tapomayukh Bhattacharjee
Robot assisted feeding: exploring autonomy with perceived errors
|18:00 – 18:30||Panel discussion|
|Allison Okamura (Stanford Univ.)
Title: Human Interface for Teleoperated Object Manipulation with a Soft Growing Robot
Abstract: Soft growing robots are proposed for use in applications such as complex manipulation tasks or navigation in disaster scenarios. Safe interaction and ease of production promote the usage of this technology, but soft robots can be challenging to teleoperate due to their unique degrees of freedom. We propose a human-centered interface that allows users to teleoperate a soft growing robot for manipulation tasks using arm movements. A study was conducted to assess the intuitiveness of the interface and the performance of our soft robot, involving a pick-and-place manipulation task. The results show that users completed the task with a success rate of 97%, achieving placement errors below 2 cm on average. These results demonstrate that our body-movement-based interface is an effective method for control of a soft growing robot manipulator. We believe that these results may be further improved with the implementation of shared autonomy protocols. By allowing the robot to participate in the execution of the task, the role of the human operator will be simplified and the different strengths of the human and the robot can be exploited.
|Tapomayukh Bhattacharjee (Univ. of Washington)
Title: Robot-assisted Feeding: Exploring Autonomy with Perceived Errors
Abstract: Robot-assisted feeding can potentially enable people with upper-body mobility impairments to eat independently. Eating free-form food is one of the most intricate manipulation tasks we perform in our daily lives, and manual teleoperation of such an intricate task which involves controlling a high-DoF robot arm with a low-DoF user interface can be very challenging and time consuming. We focus on an autonomous solution but successful robot-assisted feeding depends on reliable bite acquisition of hard-to-model deformable food items and easy bite transfer. Using insights from human studies, I will showcase algorithms and technologies that leverage multiple sensing modalities to perceive varied food item properties and determine successful strategies for bite acquisition and transfer. However, successful autonomous assistance in the real world was still challenging because of the possibility of errors in uncertain and unstructured environments. Through a study with people with mobility limitations, we found no clear preference for higher levels of autonomy but interestingly, when grouped according to their mobility limitations, ratings from participants with higher mobility limitations were correlated with lower expectations of robot performance and higher levels of autonomy even with perceived errors.
|Paolo Robuffo Giordano (CNRS/IRISA)
Title: Human-assisted robotics
Abstract: Nowadays and future robotics applications are expected to address more and more complex tasks in increasingly unstructured environments and in co-existence or co-operation with humans. Achieving full autonomy is clearly a ”holy grail” for the robotics community: however, one could easily argue that real full autonomy is, in practice, out of reach for years to come, and in some cases also not desirable. The leap between the cognitive skills (e.g., perception, decision making, general scene understanding) of us humans w.r.t. those of the most advanced nowadays robots is still huge. In most applications involving tasks in unstructured environments, uncertainty, and interaction with the physical word, human assistance is still necessary, and will probably be for the next decades. These considerations motivate research efforts into the (large) topic of shared control for complex robotics systems: on the one hand, empower robots with a large degree of autonomy for allowing them to effectively operate in non-trivial environments. On the other hand, include human users in the loop for having them in (partial) control of some aspects of the overall robot behavior. In this talk I will then review several recent results on novel shared control architectures meant to blend together diverse fields of robot autonomy (sensing, planning, control, machine learning) for providing a human operator an easy ”interface” for commanding the robot at high-level. Applications to the control of single/multiple mobile robots for remote navigation, and of manipulation systems for remote telemanipulation will be illustrated.
|Oussama Khatib (Stanford Univ.)
Title: Human-Robot Collaboration: Interfaces and Control Architecture
|Daniele Pucci (IIT)
Title: Wearable Technologies, Estimation, and Control for Human Robot Collaboration with an Application to COVID-19
Abstract: This talk overviews the technologies derived for estimating and controlling human robot collaboration (HRC) scenarios developed in the framework of the H2020 European project An.Dy. Furthermore, it will be presented how the technologies for HRC have been used to derive solutions that help maintain social distancing** and monitor COVID-19 symptoms.**[Note from the organizers: “Social distancing” is a widely used term in relation to COVID-19 to indicate that people should keep meters away from the others. Many researchers pointed out that it may be sending the wrong message and contributing to social isolation. “Physical distancing” sounds more appropriate term, it simplifies the concept with the emphasis on keeping physical distance from others.]
|Arash Ajoudani (IIT)
Title: Shared authority control of a MObile Collaborative robot Assistant (MOCA): From close proximity collaboration to remote loco-manipulation
Abstract: This talk covers the HRI2 laboratory’s recent progress in shared authority control of a mobile collaborative robot. MOCA is a new research platform developed at IIT, which is composed by a lightweight manipulator arm, a Pisa/IIT SoftHand, and a mobile platform driven by four Omni-directional wheels. The loco-manipulation behaviour of the robot is controlled by a whole-body torque controller, which takes into account the causal interactions in such a dynamic system. The planning of the loco-manipulation trajectories in close proximity collaboration with humans or remote teleoperation tasks is achieved using a shared control system, which reacts based on the human sensory and intentional inputs and physical effort, and the task requirements.
|Cristian Secchi (Univ. Modena e Reggio Emilia)
Title: Shared control for human-robot interaction: an energy based perspective
Abstract: Controlling the way energy is exchanged between among humans, robots and the environment they are interacting with is crucial for ensuring a natural and stable behavior of the overall system, despite of variations of authority in the team. In this talk, I will illustrate our recent research on energy based control for human robot(s) interaction and I will show how to develop energy based controller for a flexible and natural shared control of a human robots team.
|Leonel Rozo (Bosch center for AI)
Title: Leveraging domain knowledge for efficient learning and adaptation of robotic skills
Abstract: Robotic skills learning from human demonstrations usually allows the robot to execute tasks for a subset of different task instances. However, when this instance involves significant environment changes, failure and/or high uncertainty, the learned skills need to be adapted on the fly. Such an adaptation process needs to be safe, fast and data-efficient as the robot is a physical system interacting with the environment or a human partner, and therefore every single adaptation trial is costly. Data-efficient robot learning has been tackled from different perspectives, for example, through the use of hierarchical structures, using sample-efficient optimization methods, or by integrating prior knowledge in the skill representation or the reward function. In this talk, I will present how domain knowledge extracted from the geometry of the robot parameters can be exploited to learn, from demonstrations, variable impedance skills or time-varying profiles of manipulability ellipsoids. I will also show how this geometry information is exploited to efficiently adapt robot skills using a geometry-aware Bayesian optimization framework. Finally, I will show current applications in robotic manipulation and physical human-robot interaction that benefit from the presented approaches. Future research will highlight how variable impedance behaviors and manipulability ellipsoids may be exploited in human-robot collaboration scenarios.
|Byron Boots (Univ. of Washington & NVIDIA)
Title: Online Learning for Adaptive Robotic Systems
Abstract: There are few things more frustrating than a machine that repeats the same mistake over and over again. To contend with a complex and uncertain world, robots must learn from their mistakes and rapidly adapt to their environment. The main goal of this talk is to illustrate how machine learning can start to address some of the fundamental perceptual and control challenges involved in building intelligent robots. I’ll start by introducing an online learning perspective on robot adaptation that unifies well-known algorithms and suggests new approaches. Along the way, I’ll focus on the use of prior knowledge and expert advice to augment learning: I’ll discuss how imperfect models can be leveraged to rapidly update simple control policies and imitation can accelerate reinforcement learning. I will also show how we have applied these ideas to an autonomous “AutoRally” robot built at Georgia Tech and an off-road racing task that requires impressive sensing, speed, and agility to complete.
|Paolo Rocco (Politecnico di Milano)
Title: Occlusion-free visual servoing for the shared autonomy teleoperation of dual-arm robots
Abstract: In this talk we discuss the shared control of a dual-arm teleoperation system, where one robot is autonomous and equipped with a camera, while the other is teleoperated. We developed a unified visual servoed controller for occlusion-free teleoperation in dynamic environments. The proposed controller relies on a quadratic programming optimization formulation, that simultaneously takes into account both robot arms. While one arm is tasked with tracking the input from a user operating a master station, the camera relies on feature information to avoid occlusion and keep the teleoperated arm in the field of view. To this end, an occlusion constraint is defined in the image space based on the minimum distance criterion and made robust against noisy measurements and dynamic environment. A state machine is used to switch the control policy whenever an occlusion might occur. We validate our approach with experiments on a 14 d.o.f. dual-arm ABB YuMi robot equipped with an red, green, blue (RGB) camera and teleoperated by a 3 d.o.f. Novint Falcon device.
Univ. Napoli, Italy
Univ. Napoli, Italy
Support from IEEE RAS Technical Committee
- Human-Robot Interaction & Coordination
- Robot Learning
Call for papers and contributions
We would like to invite all the prospective participants to submit an extended abstract (up to 2 pages) to be presented at the workshop. The manuscripts should use the IEEE ICRA two-column format. Please submit a PDF copy of your manuscript through our EasyChair platform before April 24th. Papers will be selected based on their originality, relevance to the workshop topics, contributions, technical clarity, and presentation. Authors of the accepted papers will be invited to submit an extended paper to a special issue to be organized. Accepted papers require that at least one of the authors attend the workshop. This workshop is an excellent opportunity to present and discuss your ongoing work and get an early feedback from the participants. All the accepted papers will have a relevant role in the workshop. In particular, the authors will be requested to perform a teaser and a poster presentation at the workshop. Hands-on demo are highly encouraged.
The topics of interest include but are not limited to:
• Shared autonomous systems
• Modeling and learning human-robot interaction
• Shared and supervisory control
• Human-in-the-loop systems
• Collaborative and assistive robotics
• Telerobotics control and interfaces
• Haptic feedback and guidance
• Robot safety
• Co-adaptation between human and robot
• Intention recognition, skill level/gap evaluation and role allocation
• Learning from demonstration
• Applications in robotic teleoperation, mobile robotics, humanoid robotics, and medical robotics
How to contribute to the workshop
To submit the paper please follow the Easychair link: https://easychair.org/cfp/salc2020
April 24th May 20th
April 30th May 27th
May 6th May 30th
Workshop day: June 4th
Selected contributed papers
- Rahaf Rahal, Giulia Matarese, Marco Gabiccini, Alessio Artoni, Domenico Prattichizzo, Paolo Robuffo Giordano and Claudio Pacchierotti. Haptic shared control for enhanced user comfort in robotic telemanipulation SALC2020_paper_3c
- Yu She. Control and Planning of Variable Stiffness Links for Inherently Safe Physical Human Robots Interaction SALC2020_paper_4
- Marco Ferro, Claudio Gaz and Marilena Vendittelli. A framework for sensorless identification of needle-tissue interaction forces in robot-assisted biopsies SALC2020_paper_15c
- Santiago Iregui, Cristian Vergara, Joris De Schutter and Erwin Aertbeliën. Generating Reactive Virtual Guidance Fixtures for Assisted Telemanipulation Tasks SALC2020_paper_9c
- Reut Nomberg and Ilana Nisky. Human-in-the-loop stability analysis of haptic rendering of a virtual stiffness with delay – the effect of arm impedance SALC2020_paper_5
- Yoojin Oh, Shao-Wen Wu, Marc Toussaint and Jim Mainprice. Natural Gradient Shared Control SALC2020_paper_6
- Marco Tognon, Rachid Alami and Bruno Siciliano. Human Physical Guidance by a Tethered Aerial Vehicle SALC2020_paper_8c
- Jan Peters, Bani Anvari and Helge Wurdemann. Towards an Intelligent Driver Seat for Safe Autonomy Level Transitions in Autonomous Cars SALC2020_paper_1c
- Rafael Papallas, Anthony G. Cohn and Mehmet R. Dogar. Optimization-based Motion Planning with Human in The Loop forNon-Prehensile Manipulation SALC2020_paper_12
- Connor Brooks and Daniel Szafir. Perspective Taking for Shared Control SALC2020_paper_7
- Oliver Roesler. Enhancing Unsupervised Language Grounding through Online Learning SALC2020_paper_14
The workshop is supported by the European Project An.Dy.