6 citations · 7 across the 4 of their papers we have counts for
5 papers
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…
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…
Deep Reinforcement Learning for Multi-Agent Coordination
Kehinde O. Aina, Sehoon Ha
We address the challenge of coordinating multiple robots in narrow and confined environments, where congestion and interference often hinder collective task performance. Drawing in…
A Design Co-Pilot for Task-Tailored Manipulators
Jonathan Külz, Sehoon Ha, Matthias Althoff
Although robotic manipulators are used in an ever-growing range of applications, robot manufacturers typically follow a ``one-fits-all'' philosophy, employing identical manipulator…
Learning-based legged locomotion; state of the art and future perspectives
Sehoon Ha, Joonho Lee, Michiel van de Panne +3
Legged locomotion holds the premise of universal mobility, a critical capability for many real-world robotic applications. Both model-based and learning-based approaches have advan…