1 citations · 1 across the 7 of their papers we have counts for
7 papers
ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients
Yixiao Li, Tifanny Portela, Jordis Herrmann +2
Neural Motion Planners (NMPs) enable fast reactive motion generation, but adapting them to new environments typically requires recollecting large expert datasets, which is computat…
Grounding Generative Policies in Physics: Optimization-Guided Diffusion for Robot Control
Sabrina Bodmer, René Zurbrügg, Tifanny Portela +5
Diffusion models sample effectively from high-dimensional, multimodal distributions, but their outputs may violate deployment constraints. For task-space robot policies, generated…
Geometric Action Model for Robot Policy Learning
Jisang Han, Seonghu Jeon, Jaewoo Jung +7
Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical world. Recent vision-language-acti…
VR-DAgger: Immersive VR for Dexterous Data Collection and Uncertainty-Guided On-Policy Correction
René Zurbrügg, Tifanny Portela, Arjun Bhardwaj +3
Learning from demonstrations is effective for robotic manipulation, but collecting sufficient task-specific data remains a major bottleneck. Under distribution shift, small errors…
Pretraining in Actor-Critic Reinforcement Learning for Locomotion
Jiale Fan, Andrei Cramariuc, Tifanny Portela +1
The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years. However, in the domain of reinforcemen…
Whole-body End-Effector Pose Tracking
Tifanny Portela, Andrei Cramariuc, Mayank Mittal +1
Combining manipulation with the mobility of legged robots is essential for a wide range of robotic applications. However, integrating an arm with a mobile base significantly increa…
Learning Force Control for Legged Manipulation
Tifanny Portela, Gabriel B. Margolis, Yandong Ji +1
Controlling contact forces during interactions is critical for locomotion and manipulation tasks. While sim-to-real reinforcement learning (RL) has succeeded in many contact-rich p…