15 citations · 23 across the 10 of their papers we have counts for
11 papers
PetriBench: Benchmarking LLM Reasoning over Dynamic State Spaces
Pyrros Koussios, Benjamin Jäger, John Hua Yao +3
Characterizing LLM reasoning remains an open challenge, as many existing benchmarks isolate specific reasoning skills, rely on external knowledge, or are costly to extend. We intro…
Cross-Embodiment Transfer via Behavior-Aligned Representations
Ajay Sridhar, Jensen Gao, Jonathan Yang +3
Recent progress in large-scale imitation learning for robot manipulation has been driven by leveraging datasets across a wide range of robot embodiments. However, achieving signifi…
DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
Jadelynn Dao, Milan Ganai, Yasmina Abukhadra +7
Vision-Language Models (VLMs) are increasingly deployed as high-level planners for embodied agents, with an emerging strategy of scaling test-time compute to improve capability. Ho…
MemER: Scaling Up Memory for Robot Control via Experience Retrieval
Ajay Sridhar, Jennifer Pan, Satvik Sharma +1
Humans routinely rely on memory to perform tasks, yet most robot policies lack this capability; our goal is to endow robot policies with the same ability. Naively conditioning on l…
LeLaN: Learning A Language-Conditioned Navigation Policy from In-the-Wild Videos
Noriaki Hirose, Catherine Glossop, Ajay Sridhar +3
The world is filled with a wide variety of objects. For robots to be useful, they need the ability to find arbitrary objects described by people. In this paper, we present LeLaN(Le…
SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigation
Noriaki Hirose, Dhruv Shah, Kyle Stachowicz +2
Autonomous self-improving robots that interact and improve with experience are key to the real-world deployment of robotic systems. In this paper, we propose an online learning met…
NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration
Ajay Sridhar, Dhruv Shah, Catherine Glossop +1
Robotic learning for navigation in unfamiliar environments needs to provide policies for both task-oriented navigation (i.e., reaching a goal that the robot has located), and task-…
ViNT: A Foundation Model for Visual Navigation
Dhruv Shah, Ajay Sridhar, Nitish Dashora +4
General-purpose pre-trained models ("foundation models") have enabled practitioners to produce generalizable solutions for individual machine learning problems with datasets that a…
SACSoN: Scalable Autonomous Control for Social Navigation
Noriaki Hirose, Dhruv Shah, Ajay Sridhar +1
Machine learning provides a powerful tool for building socially compliant robotic systems that go beyond simple predictive models of human behavior. By observing and understanding…
ExAug: Robot-Conditioned Navigation Policies via Geometric Experience Augmentation
Noriaki Hirose, Dhruv Shah, Ajay Sridhar +1
Machine learning techniques rely on large and diverse datasets for generalization. Computer vision, natural language processing, and other applications can often reuse public datas…
GNM: A General Navigation Model to Drive Any Robot
Dhruv Shah, Ajay Sridhar, Arjun Bhorkar +2
Learning provides a powerful tool for vision-based navigation, but the capabilities of learning-based policies are constrained by limited training data. If we could combine data fr…