254 citations · 267 across the 18 of their papers we have counts for
6 papers · 1 filter
Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation
Jean-Pierre Sleiman, Mayank Mittal, Marco Hutter
Reinforcement learning (RL) often necessitates a meticulous Markov Decision Process (MDP) design tailored to each task. This work aims to address this challenge by proposing a syst…
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…
Perceptive Pedipulation with Local Obstacle Avoidance
Jonas Stolle, Philip Arm, Mayank Mittal +1
Pedipulation leverages the feet of legged robots for mobile manipulation, eliminating the need for dedicated robotic arms. While previous works have showcased blind and task-specif…
ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity
Qinxi Yu, Masoud Moghani, Karthik Dharmarajan +8
Physics-based simulations have accelerated progress in robot learning for driving, manipulation, and locomotion. Yet, a fast, accurate, and robust surgical simulation environment r…
Symmetry Considerations for Learning Task Symmetric Robot Policies
Mayank Mittal, Nikita Rudin, Victor Klemm +2
Symmetry is a fundamental aspect of many real-world robotic tasks. However, current deep reinforcement learning (DRL) approaches can seldom harness and exploit symmetry effectively…
Pedipulate: Enabling Manipulation Skills using a Quadruped Robot's Leg
Philip Arm, Mayank Mittal, Hendrik Kolvenbach +1
Legged robots have the potential to become vital in maintenance, home support, and exploration scenarios. In order to interact with and manipulate their environments, most legged r…