most citedAgile Continuous Jumping in Discontinuous Terrains

1 citations · 1 across the 8 of their papers we have counts for

collaborators

8 papers

cs.RO2025

Using Non-Expert Data to Robustify Imitation Learning via Offline Reinforcement Learning

Kevin Huang, Rosario Scalise, Cleah Winston +9

Imitation learning has proven effective for training robots to perform complex tasks from expert human demonstrations. However, it remains limited by its reliance on high-quality,…

cs.RO2025

VAMOS: A Hierarchical Vision-Language-Action Model for Capability-Modulated and Steerable Navigation

Mateo Guaman Castro, Sidharth Rajagopal, Daniel Gorbatov +9

A fundamental challenge in robot navigation lies in learning policies that generalize across diverse environments while conforming to the unique physical constraints and capabiliti…

cs.RO2025

Model Predictive Adversarial Imitation Learning for Planning from Observation

Tyler Han, Yanda Bao, Bhaumik Mehta +8

Human demonstration data is often ambiguous and incomplete, motivating imitation learning approaches that also exhibit reliable planning behavior. A common paradigm to perform plan…

cs.RO2025

Towards Embodiment Scaling Laws in Robot Locomotion

Bo Ai, Liu Dai, Nico Bohlinger +7

Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…

cs.RO2025

Long Range Navigator (LRN): Extending robot planning horizons beyond metric maps

Matt Schmittle, Rohan Baijal, Nathan Hatch +6

A robot navigating an outdoor environment with no prior knowledge of the space must rely on its local sensing to perceive its surroundings and plan. This can come in the form of a…

cs.RO2025

Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation

Octi Zhang, Quanquan Peng, Rosario Scalise +1

Developing robotic agents that can perform well in diverse environments while showing a variety of behaviors is a key challenge in AI and robotics. Traditional reinforcement learni…