132 citations · 572 across the 61 of their papers we have counts for
6 papers · 2 filters
Not All Actions Are Created Equal: Bayesian Optimal Experimental Design for Safe and Optimal Nonlinear System Identification
Parker Ewen, Gitesh Gunjal, Joey Wilson +3
Uncertainty in state or model parameters is common in robotics and typically handled by acquiring system measurements that yield information about the uncertain quantities of inter…
Convex Geometric Motion Planning on Lie Groups via Moment Relaxation
Sangli Teng, Ashkan Jasour, Ram Vasudevan +1
This paper reports a novel result: with proper robot models on matrix Lie groups, one can formulate the kinodynamic motion planning problem for rigid body systems as \emph{exact} p…
Sense, Imagine, Act: Multimodal Perception Improves Model-Based Reinforcement Learning for Head-to-Head Autonomous Racing
Elena Shrestha, Chetan Reddy, Hanxi Wan +2
Model-based reinforcement learning (MBRL) techniques have recently yielded promising results for real-world autonomous racing using high-dimensional observations. MBRL agents, such…
Reachability-based Trajectory Design with Neural Implicit Safety Constraints
Jonathan Michaux, Qingyi Chen, Yongseok Kwon +1
Generating safe motion plans in real-time is a key requirement for deploying robot manipulators to assist humans in collaborative settings. In particular, robots must satisfy stric…
RADIUS: Risk-Aware, Real-Time, Reachability-Based Motion Planning
Jinsun Liu, Challen Enninful Adu, Lucas Lymburner +3
Deterministic methods for motion planning guarantee safety amidst uncertainty in obstacle locations by trying to restrict the robot from operating in any possible location that an…
Can't Touch This: Real-Time, Safe Motion Planning and Control for Manipulators Under Uncertainty
Jonathan Michaux, Patrick Holmes, Bohao Zhang +7
Ensuring safe, real-time motion planning in arbitrary environments requires a robotic manipulator to avoid collisions, obey joint limits, and account for uncertainties in the mass…