978 citations · 4.5k across the 70 of their papers we have counts for
12 papers · 1 filter
PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation
Shizhe Chen, Ricardo Garcia, Cordelia Schmid +1
The ability for robots to comprehend and execute manipulation tasks based on natural language instructions is a long-term goal in robotics. The dominant approaches for language-gui…
Robust Visual Sim-to-Real Transfer for Robotic Manipulation
Ricardo Garcia, Robin Strudel, Shizhe Chen +3
Learning visuomotor policies in simulation is much safer and cheaper than in the real world. However, due to discrepancies between the simulated and real data, simulator-trained po…
Learning Video-Conditioned Policies for Unseen Manipulation Tasks
Elliot Chane-Sane, Cordelia Schmid, Ivan Laptev
The ability to specify robot commands by a non-expert user is critical for building generalist agents capable of solving a large variety of tasks. One convenient way to specify the…
Contact Models in Robotics: a Comparative Analysis
Quentin Le Lidec, Wilson Jallet, Louis Montaut +3
Physics simulation is ubiquitous in robotics. Whether in model-based approaches (e.g., trajectory optimization), or model-free algorithms (e.g., reinforcement learning), physics si…
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…
Leveraging Randomized Smoothing for Optimal Control of Nonsmooth Dynamical Systems
Quentin Le Lidec, Fabian Schramm, Louis Montaut +3
Optimal control (OC) algorithms such as Differential Dynamic Programming (DDP) take advantage of the derivatives of the dynamics to efficiently control physical systems. Yet, in th…
Learning Object Manipulation Skills via Approximate State Estimation from Real Videos
Vladimír Petrík, Makarand Tapaswi, Ivan Laptev +1
Humans are adept at learning new tasks by watching a few instructional videos. On the other hand, robots that learn new actions either require a lot of effort through trial and err…
Learning Obstacle Representations for Neural Motion Planning
Robin Strudel, Ricardo Garcia, Justin Carpentier +3
Motion planning and obstacle avoidance is a key challenge in robotics applications. While previous work succeeds to provide excellent solutions for known environments, sensor-based…
Learning visual policies for building 3D shape categories
Alexander Pashevich, Igor Kalevatykh, Ivan Laptev +1
Manipulation and assembly tasks require non-trivial planning of actions depending on the environment and the final goal. Previous work in this domain often assembles particular ins…
Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning
Yann Labbé, Sergey Zagoruyko, Igor Kalevatykh +4
We address the problem of visually guided rearrangement planning with many movable objects, i.e., finding a sequence of actions to move a set of objects from an initial arrangement…