132 citations · 752 across the 23 of their papers we have counts for
27 papers
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…
Modeling Dynamic Environments with Scene Graph Memory
Andrey Kurenkov, Michael Lingelbach, Tanmay Agarwal +7
Embodied AI agents that search for objects in large environments such as households often need to make efficient decisions by predicting object locations based on partial informati…
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…
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation
Fei Xia, Chengshu Li, Roberto Martín-Martín +3
Many Reinforcement Learning (RL) approaches use joint control signals (positions, velocities, torques) as action space for continuous control tasks. We propose to lift the action s…
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…
JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments
Roberto Martín-Martín, Mihir Patel, Hamid Rezatofighi +5
We present JRDB, a novel egocentric dataset collected from our social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including s…
Regression Planning Networks
Danfei Xu, Roberto Martín-Martín, De-An Huang +3
Recent learning-to-plan methods have shown promising results on planning directly from observation space. Yet, their ability to plan for long-horizon tasks is limited by the accura…
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…
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks
Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín +3
Predicting the future trajectories of multiple interacting agents in a scene has become an increasingly important problem for many different applications ranging from control of au…
Improving Social Awareness Through DANTE: A Deep Affinity Network for Clustering Conversational Interactants
Mason Swofford, John Charles Peruzzi, Nathan Tsoi +4
We propose a data-driven approach to detect conversational groups by identifying spatial arrangements typical of these focused social encounters. Our approach uses a novel Deep Aff…
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…