132 citations · 752 across the 23 of their papers we have counts for
19 papers · 1 filter
BiFlex: A Passive Bimodal Stiffness Flexible Wrist for Manipulation in Unstructured Environments
Gu-Cheol Jeong, Stefano Dalla Gasperina, Ashish D. Deshpande +2
Robotic manipulation in unstructured, humancentric environments poses a dual challenge: achieving the precision need for delicate free-space operation while ensuring safety during…
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…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning
Jiaheng Hu, Rose Hendrix, Ali Farhadi +5
In recent years, the Robotics field has initiated several efforts toward building generalist robot policies through large-scale multi-task Behavior Cloning. However, direct deploym…
TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
Shivin Dass, Wensi Ai, Yuqian Jiang +6
A critical bottleneck limiting imitation learning in robotics is the lack of data. This problem is more severe in mobile manipulation, where collecting demonstrations is harder tha…
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation
Chengshu Li, Ruohan Zhang, Josiah Wong +32
We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an extensive s…
Model-Based Runtime Monitoring with Interactive Imitation Learning
Huihan Liu, Shivin Dass, Roberto Martín-Martín +1
Robot learning methods have recently made great strides, but generalization and robustness challenges still hinder their widespread deployment. Failing to detect and address potent…
Differentiable Factor Graph Optimization for Learning Smoothers
Brent Yi, Michelle A. Lee, Alina Kloss +2
A recent line of work has shown that end-to-end optimization of Bayesian filters can be used to learn state estimators for systems whose underlying models are difficult to hand-des…
LASER: Learning a Latent Action Space for Efficient Reinforcement Learning
Arthur Allshire, Roberto Martín-Martín, Charles Lin +3
The process of learning a manipulation task depends strongly on the action space used for exploration: posed in the incorrect action space, solving a task with reinforcement learni…
Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning
Claudia Pérez-D'Arpino, Can Liu, Patrick Goebel +2
Navigating fluently around pedestrians is a necessary capability for mobile robots deployed in human environments, such as buildings and homes. While research on social navigation…
Multimodal Sensor Fusion with Differentiable Filters
Michelle A. Lee, Brent Yi, Roberto Martín-Martín +2
Leveraging multimodal information with recursive Bayesian filters improves performance and robustness of state estimation, as recursive filters can combine different modalities acc…
Probabilistic Visual Navigation with Bidirectional Image Prediction
Noriaki Hirose, Shun Taguchi, Fei Xia +4
Humans can robustly follow a visual trajectory defined by a sequence of images (i.e. a video) regardless of substantial changes in the environment or the presence of obstacles. We…
Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations
Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín +2
Imitation learning is an effective and safe technique to train robot policies in the real world because it does not depend on an expensive random exploration process. However, due…
Interactive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environments
Fei Xia, William B. Shen, Chengshu Li +6
We present Interactive Gibson Benchmark, the first comprehensive benchmark for training and evaluating Interactive Navigation: robot navigation strategies where physical interactio…
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks
Roberto Martín-Martín, Michelle A. Lee, Rachel Gardner +3
Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive results in recent years. While many studies in RL focus on varying the observation space or re…
Deep Local Trajectory Replanning and Control for Robot Navigation
Ashwini Pokle, Roberto Martín-Martín, Patrick Goebel +8
We present a navigation system that combines ideas from hierarchical planning and machine learning. The system uses a traditional global planner to compute optimal paths towards a…
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter
Michael Danielczuk, Andrey Kurenkov, Ashwin Balakrishna +6
When operating in unstructured environments such as warehouses, homes, and retail centers, robots are frequently required to interactively search for and retrieve specific objects…
Deep Visual MPC-Policy Learning for Navigation
Noriaki Hirose, Fei Xia, Roberto Martin-Martin +2
Humans can routinely follow a trajectory defined by a list of images/landmarks. However, traditional robot navigation methods require accurate mapping of the environment, localizat…
The RBO Dataset of Articulated Objects and Interactions
Roberto Martín-Martín, Clemens Eppner, Oliver Brock
We present a dataset with models of 14 articulated objects commonly found in human environments and with RGB-D video sequences and wrenches recorded of human interactions with them…