Autonomy Infused Teleoperation with Application to BCI Manipulation

Abstract: Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain- Computer Interfaces (BCIs) exacerbates these problems through especially noisy and erratic low-dimensional motion commands due to the difficulty in decoding neural activity. We introduce a general framework to address these challenges through a combination of computer vision, user intent inference, and arbitration between the human input and autonomous control schemes. Adjustable levels of assistance allow the system to balance the operator’s capabilities and feelings of comfort and control while compensating for a task’s difficulty. We present experimental results demonstrating significant performance improvement using the shared-control assistance framework on adapted rehabilitation benchmarks with two subjects implanted with intracortical brain-computer interfaces controlling a seven degree-of-freedom robotic manipulator as a prosthetic. Our results further indicate that shared assistance mitigates perceived user difficulty and even enables successful performance on previously infeasible tasks. We showcase the extensibility of our architecture with applications to quality-of-life tasks such as opening a door, pouring liquids from containers, and manipulation with novel objects in densely cluttered environments.

Authors: Katharina MuellingArun VenkatramanJean-Sebastien Valois, John Downey, Jeffrey Weiss, Shervin JavdaniMartial Hebert, Andrew B. Schwartz, Jennifer L. Collinger, and J. Andrew (Drew) Bagnell

Online Bellman Residual Algorithms with Predictive Error Guarantees 

Abstract: We establish a connection between optimizing the Bellman Residual and worst case long-term predictive error. In the online learning framework, learning takes place over a sequence of trials with the goal of predicting a future discounted sum of rewards. Our analysis shows that, together with a stability assumption, any no-regret online learning algorithm that minimizes Bellman error ensures small prediction error. No statistical assumptions are made on the sequence of observations, which could be non-Markovian or even adversarial. Moreover, the analysis is independent of the particular form of function approximation and the particular (stable) no-regret approach taken. Our approach thus establishes a broad new family of provably sound algorithms for Bellman Residual-based learning and provides a generalization of previous worst-case result for minimizing predictive error. We investigate the potential advantages of some of this family both theoretically and empirically on benchmark problems.

Authors: Wen Sun and J. Andrew (Drew) Bagnell

Autonomous Robotic Manipulation – Stack ‘n’ Smash Exhibit at the National Air and Space Museum

December 17, 2013

Watch as our ARM-S system autonomously stacks blocks as demonstrated on a touch based GUI!

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Congratulations, Dr. Ross!

July 10, 2013

Congratulations to Stephane for successfully defending his thesis on “Interactive Learning for Sequential Decisions and Predictions” A copy of Stephane’s thesis is available here

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Congratulations, Dr. Munoz!

June 13, 2013

Congratulations to Dan for successfully defending his thesis on “Inference Machines: Parsing Scenes via Iterated Predictions”. His thesis is available at

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ARM-S Project covered on the NYT

March 31, 2013

The New York Times has covered an article on the ARM-S project where we perform the task of changing a tire autonomously:  

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Congratulations Kris for ECCV ’12 Best Paper Honorable Mention!

October 16, 2012

Title: Activity Forecasting Authors: Kris Kitani, Brian Ziebart, Drew Bagnell and Martial Hebert Abstract: We address the task of inferring the future actions of people from noisy visual input. We denote this task activity forecasting. To achieve accurate activity forecasting, our approach models the effect of the physical environment on the choice of human actions. [...]

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BIRD MURI Star Wars video

September 10, 2012

The BIRD MURI team has recently published this video to showcase it’s research on autonomous avoidance of trees by micro UAVs in forests. For more information, head to the BIRD MURI page!

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New Arrivals!

August 20, 2012

This year we welcome Dov Katz, working with Drew Bagnell and Tony Stentz on interactive perception for manipulation in unstructured environments, Nick Rhinehart as a research programmer working on the VMR and finder projects, Andreas Wendel, a visiting PhD student from the Graz University of Technology as a visiting scholar working on the BURD MURI project [...]

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Congrats Kevin and Brian! ICML 2011 Best Paper Award

July 18, 2011

Computational Rationalization: The Inverse Equilibrium Problem Abstract:Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task. When restricted to the single-agent decision-theoretic setting, inverse optimal control techniques assume that observed behavior is an approximately optimal solution to an unknown decision problem. These techniques learn a utility function that [...]

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