5 papers
Generative Models From and For Sampling-Based MPC: A Bootstrapped Approach For Adaptive Contact-Rich Manipulation
Lara Brudermüller, Brandon Hung, Xinghao Zhu +4
We present a generative predictive control (GPC) framework that amortizes sampling-based Model Predictive Control (SPC) by bootstrapping it with conditional flow-matching models tr…
Enhancing Joint Human-AI Inference in Robot Missions: A Confidence-Based Approach
Duc-An Nguyen, Clara Colombatto, Steve Fleming +3
Joint human-AI inference holds immense potential to improve outcomes in human-supervised robot missions. Current day missions are generally in the AI-assisted setting, where the hu…
Joint Decision-Making in Robot Teleoperation: When are Two Heads Better Than One?
Duc-An Nguyen, Raunak Bhattacharyya, Clara Colombatto +3
Operators working with robots in safety-critical domains have to make decisions under uncertainty, which remains a challenging problem for a single human operator. An open question…
Robust Pushing: Exploiting Quasi-static Belief Dynamics and Contact-informed Optimization
Julius Jankowski, Lara Brudermüller, Nick Hawes +1
Non-prehensile manipulation such as pushing is typically subject to uncertain, non-smooth dynamics. However, modeling the uncertainty of the dynamics typically results in intractab…
CC-VPSTO: Chance-Constrained Via-Point-Based Stochastic Trajectory Optimisation for Online Robot Motion Planning under Uncertainty
Lara Brudermüller, Guillaume Berger, Julius Jankowski +3
Reliable robot autonomy hinges on decision-making systems that account for uncertainty without imposing overly conservative restrictions on the robot's action space. We introduce C…