Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

https://web.cs.wpi.edu/~rail/files/ArgallEtAlRAS09.pdf

A survey of robot learning from demonstration

Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

/pdf/reinforcement-learning-in-robotics-a-survey-4w4397qx9i.pdf

Reinforcement learning in robotics: A survey

Mr M, a patient with semantic dementia - a neurodegenerative disease that is characterized by the gradual deterioration of semantic memory - was being driven through the countryside to visit a friend and was able to remind his wife where to turn along the not-recently-travelled route. Then, pointing at the sheep in the field, he asked her "What are those things?" Prior to the onset of symptoms in his late 40s, this man had normal semantic memory. What has gone wrong in his brain to produce this dramatic and selective erosion of conceptual knowledge?

Where do you know what you know? The representation of semantic knowledge in the human brain.

http://vpnl.stanford.edu/papers/kgs_etal_TICS.06.pdf

Repetition and the brain: neural models of stimulus-specific effects

Robust motion control algorithms are fundamental to the successful, autonomous operation of mobile robots. Motion control is known to be a difficult problem, and is often dictated by a policy, or state-action mapping. In this chapter, we present an approach for the refinement of mobile robot motion control policies, that incorporates corrective feedback from a human teacher. The target application domain of this work is the low-level motion control of a mobile robot. Within such domains, the rapid sampling rate and continuous action space of policies are both key challenges to providing policy corrections. To address these challenges, we contribute advice-operators as a corrective feedback form suitable for providing continuous-valued corrections, and Focused Feedback For Mobile Robot Policies (F3MRP) as a framework suitable for providing feedback on policies sampled at a high frequency. Under our approach, policies refined through teacher feedback are initially derived using Learning from Demonstration (LfD) techniques, which generalize a policy from example task executions by a teacher. We apply our techniques within the Advice-Operator Policy Improvement (A-OPI) algorithm, validated on a Segway RMP robot within a motion control domain. A-OPI refines LfD policies by correcting policy performance via advice-operators and F3MRP. Within our validation domain, policy performance is found to improve with corrective teacher feedback, and moreover to be similar or superior to that of policies provided with more teacher demonstrations.

/pdf/mobile-robot-motion-control-from-demonstration-and-5an0t3yfm0.pdf

Mobile Robot Motion Control from Demonstration and Corrective Feedback

We present in this paper a novel framework for the design of a modular and adaptive partial-autonomy wheelchair. Our design in particular aims to address hurdles to the adoption of partial-autonomy wheelchairs within general society. In this experimental work, a single assistance module (assisted doorway traversal) is evaluated, with arbitration between multiple goals (from multiple detected doors) and multiple control signals (from an autonomous path planner, and the human user). The experimental work provides the foundation and proof-of-concept for the technical components of our proposed modular and adaptive wheelchair robot. The system is evaluated within multiple environmental scenarios and shows good performance.

Modular and Adaptive Wheelchair Automation

In this new era of space exploration where human-robot teams are envisioned maintaining a long-term presence on other planets, effective coordination of these teams is paramount. Three critical research challenges that must be solved to realize this vision are the human-robot team challenge, the pickup-team challenge, and the effective humanrobot communication challenge. In this paper, we address these challenges, propose a novel approach towards solving these challenges, and situate our approach in the newly introduced treasure hunt domain.

/pdf/dynamically-formed-human-robot-teams-performing-coordinated-4fh2izyay2.pdf

Dynamically formed human-robot teams performing coordinated tasks

In this paper, we focus on human-robot interaction in a team task where we identify the need for peer-to-peer (P2P) teamwork, with no fixed hierarchy for decision making between robots and humans. Instead, all team members are equal participants and decision making is truly distributed. We have fully developed a P2P team within Segway Soccer, a research domain, built upon Robocup robot soccer, that we have introduced to explore the challenge of P2P coordination in human-robot teams with dynamic, adversarial tasks. We recently participated in the first Segway Soccer games between two competing teams at the 2005 RoboCup US Open. We believe these games are the first ever between two human-robot P2P teams. Based on the competition, we realized two different approaches to P2P teams. We present our robot-centric approach to P2P team coordination and contrast it to the human-centric approach of the opponent team.

http://www.cs.cmu.edu/~mmv/papers/05TR-segways.pdf

The first segway soccer experience: towards peer-to-peer human-robot teams

There are basic manuvering tasks with a powered wheelchair, like docking under a table and passage through a doorway or narrow hallway, which can be difficult for users with severe motor impairments—not only because of limitations in their own motor control, but also because of limitations in the control interfaces available to them. Robot automation can help transfer some of this control burden from the user to the machine. This work presents an algorithm for the automated detection of safe docking locations at rectangular and circular docking structures (tables, desks) with proper alignment infor- mation using 3D point cloud data. The safe docking locations can then be provided as goals to an autonomous path planner, within the context of providing adaptive driving assistance for powered wheelchair users. We evaluate the performance of our algorithm with systematic testing on several docking structures, observed from varied viewpoints.

Brenna D. Argall

Papers

Mobile Robot Motion Control from Demonstration and Corrective Feedback

Modular and Adaptive Wheelchair Automation

Dynamically formed human-robot teams performing coordinated tasks

The first segway soccer experience: towards peer-to-peer human-robot teams

Automated Perception of Safe Docking Locations with Alignment Information for Assistive Wheelchairs