16 citations · 79 across the 61 of their papers we have counts for
20 papers · 1 filter
Residual Compliant Policy: Learning Compliant Policies from Few Demonstrations
Chunyu Jiang, Yichen Cai, Shuai Li +6
Contact-rich manipulation requires a robot to be precise in free space to establish the intended contacts, and compliant once in contact to avoid excessive forces. Imitation learni…
Learning to Explore Hidden Kinematics for Articulated Object Manipulation
Ruiyao Liu, Boshu Lei, Zhuoyang Pan +1
The kinematics of an articulated object is often ambiguous from vision alone. Interaction resolves the ambiguity, and active perception methods exploit this by searching for the si…
OmniGuide: Universal Guidance Fields for Enhancing Generalist Robot Policies
Yunzhou Song, Long Le, Yong-Hyun Park +7
Vision-language-action(VLA) models have shown great promise as generalist policies for a large range of relatively simple tasks. However, they demonstrate limited performance on mo…
A Multi-Robot Platform for Robotic Triage Combining Onboard Sensing and Foundation Models
Jason Hughes, Marcel Hussing, Edward Zhang +24
This report presents a heterogeneous robotic system designed for remote primary triage in mass-casualty incidents (MCIs). The system employs a coordinated air-ground team of unmann…
Maestro: Orchestrating Robotics Modules with Vision-Language Models for Zero-Shot Generalist Robots
Junyao Shi, Rujia Yang, Kaitian Chao +9
Today's best-explored routes towards generalist robots center on collecting ever larger "observations-in actions-out" robotics datasets to train large end-to-end models, copying a…
ActiveGrasp: Information-Guided Active Grasping with Calibrated Energy-based Model
Boshu Lei, Wen Jiang, Kostas Daniilidis
Grasping in a densely cluttered environment is a challenging task for robots. Previous methods tried to solve this problem by actively gathering multiple views before grasp pose ge…
Active Next-Best-View Optimization for Risk-Averse Path Planning
Amirhossein Mollaei Khass, Guangyi Liu, Vivek Pandey +4
Safe navigation in uncertain environments requires planning methods that integrate risk aversion with active perception. In this work, we present a unified framework that refines a…
HELIOS: Hierarchical Exploration for Language-Grounded Interaction in Open Scenes
Katrina Ashton, Chahyon Ku, Shrey Shah +5
Language-specified mobile manipulation tasks in novel environments simultaneously face challenges interacting with a scene which is only partially observed, grounding semantic info…
RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies
Pranav Atreya, Karl Pertsch, Tony Lee +29
Comprehensive, unbiased, and comparable evaluation of modern generalist policies is uniquely challenging: existing approaches for robot benchmarking typically rely on heavy standar…
Neural Inertial Odometry from Lie Events
Royina Karegoudra Jayanth, Yinshuang Xu, Evangelos Chatzipantazis +2
Neural displacement priors (NDP) can reduce the drift in inertial odometry and provide uncertainty estimates that can be readily fused with off-the-shelf filters. However, they fai…
Symmetries-enhanced Multi-Agent Reinforcement Learning
Nikolaos Bousias, Stefanos Pertigkiozoglou, Kostas Daniilidis +1
Multi-agent reinforcement learning has emerged as a powerful framework for enabling agents to learn complex, coordinated behaviors but faces persistent challenges regarding its gen…
Multimodal LLM Guided Exploration and Active Mapping using Fisher Information
Wen Jiang, Boshu Lei, Katrina Ashton +1
We present an active mapping system that plans for both long-horizon exploration goals and short-term actions using a 3D Gaussian Splatting (3DGS) representation. Existing methods…
Next Best Sense: Guiding Vision and Touch with FisherRF for 3D Gaussian Splatting
Matthew Strong, Boshu Lei, Aiden Swann +3
We propose a framework for active next best view and touch selection for robotic manipulators using 3D Gaussian Splatting (3DGS). 3DGS is emerging as a useful explicit 3D scene rep…
EqNIO: Subequivariant Neural Inertial Odometry
Royina Karegoudra Jayanth, Yinshuang Xu, Ziyun Wang +3
Neural networks are seeing rapid adoption in purely inertial odometry, where accelerometer and gyroscope measurements from commodity inertial measurement units (IMU) are used to re…
Scalable Networked Feature Selection with Randomized Algorithm for Robot Navigation
Vivek Pandey, Arash Amini, Guangyi Liu +4
We address the problem of sparse selection of visual features for localizing a team of robots navigating an unknown environment, where robots can exchange relative position measure…
Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies
Bo Wu, Bruce D. Lee, Kostas Daniilidis +2
Large-scale robotic policies trained on data from diverse tasks and robotic platforms hold great promise for enabling general-purpose robots; however, reliable generalization to ne…
Beyond Uncertainty: Risk-Aware Active View Acquisition for Safe Robot Navigation and 3D Scene Understanding with FisherRF
Guangyi Liu, Wen Jiang, Boshu Lei +3
The active view acquisition problem has been extensively studied in the context of robot navigation using NeRF and 3D Gaussian Splatting. To enhance scene reconstruction efficiency…
Learning to Navigate in Turbulent Flows with Aerial Robot Swarms: A Cooperative Deep Reinforcement Learning Approach
Diego Patiño, Siddharth Mayya, Juan Calderon +2
Aerial operation in turbulent environments is a challenging problem due to the chaotic behavior of the flow. This problem is made even more complex when a team of aerial robots is…
Uncertainty-driven Planner for Exploration and Navigation
Georgios Georgakis, Bernadette Bucher, Anton Arapin +3
We consider the problems of exploration and point-goal navigation in previously unseen environments, where the spatial complexity of indoor scenes and partial observability constit…
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets
Frederik Ebert, Yanlai Yang, Karl Schmeckpeper +5
Robot learning holds the promise of learning policies that generalize broadly. However, such generalization requires sufficiently diverse datasets of the task of interest, which ca…