4 citations · 10 across the 11 of their papers we have counts for
4 papers · 1 filter
Reward as Observation: Learning Reward-Based Policies for Rapid Adaptation
Morgan Byrd, Maks Sorokin, Jacob Blevins +2
This paper explores a reward-based policy to achieve zero-shot transfer between source and target environments with completely different observation spaces. While humans can demons…
Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL
Martin Schuck, Maks Sorokin, Simone Manni +5
Integrating locomotion and manipulation is essential for robot autonomy, but scaling standard Reinforcement Learning (RL) to complex tasks is severely bottlenecked by the slow, man…
Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation
John Z. Zhang, Maks Sorokin, Jan Brüdigam +14
This paper presents a sim-to-real approach that enables legged robots to dynamically manipulate large and heavy objects with whole-body dexterity. Our key insight is that by perfor…
AdaptManip: Learning Adaptive Whole-Body Object Lifting and Delivery with Online Recurrent State Estimation
Morgan Byrd, Donghoon Baek, Kartik Garg +5
This paper presents Adaptive Whole-body Loco-Manipulation, AdaptManip, a fully autonomous framework for humanoid robots to perform integrated navigation, object lifting, and delive…