44 citations · 125 across the 11 of their papers we have counts for
3 papers · 2 filters
Enforcing the consensus between Trajectory Optimization and Policy Learning for precise robot control
Quentin Le Lidec, Wilson Jallet, Ivan Laptev +2
Reinforcement learning (RL) and trajectory optimization (TO) present strong complementary advantages. On one hand, RL approaches are able to learn global control policies directly…
Instruction-driven history-aware policies for robotic manipulations
Pierre-Louis Guhur, Shizhe Chen, Ricardo Garcia +3
In human environments, robots are expected to accomplish a variety of manipulation tasks given simple natural language instructions. Yet, robotic manipulation is extremely challeng…
Augmenting differentiable physics with randomized smoothing
Quentin Le Lidec, Louis Montaut, Cordelia Schmid +2
In the past few years, following the differentiable programming paradigm, there has been a growing interest in computing the gradient information of physical processes (e.g., physi…