40 citations · 71 across the 16 of their papers we have counts for
7 papers · 1 filter
Positive-Unlabeled Constraint Learning for Inferring Nonlinear Continuous Constraints Functions from Expert Demonstrations
Baiyu Peng, Aude Billard
Planning for diverse real-world robotic tasks necessitates to know and write all constraints. However, instances exist where these constraints are either unknown or challenging to…
Learning Constraint Network from Demonstrations via Positive-Unlabeled Learning with Memory Replay
Baiyu Peng, Aude Billard
Planning for a wide range of real-world tasks necessitates to know and write all constraints. However, instances exist where these constraints are either unknown or challenging to…
Hybrid Quadratic Programming -- Pullback Bundle Dynamical Systems Control
Bernardo Fichera, Aude Billard
Dynamical System (DS)-based closed-loop control is a simple and effective way to generate reactive motion policies that well generalize to the robotic workspace, while retaining st…
Passive Obstacle Aware Control to Follow Desired Velocities
Lukas Huber, Thibaud Trinca, Jean-Jacques Slotine +1
Evaluating and updating the obstacle avoidance velocity for an autonomous robot in real-time ensures robustness against noise and disturbances. A passive damping controller can obt…
Action Contextualization: Adaptive Task Planning and Action Tuning using Large Language Models
Sthithpragya Gupta, Kunpeng Yao, Loïc Niederhauser +1
Large Language Models (LLMs) present a promising frontier in robotic task planning by leveraging extensive human knowledge. Nevertheless, the current literature often overlooks the…
Learning Dynamical Systems Encoding Non-Linearity within Space Curvature
Bernardo Fichera, Aude Billard
Dynamical Systems (DS) are an effective and powerful means of shaping high-level policies for robotics control. They provide robust and reactive control while ensuring the stabilit…