327 citations · 1.3k across the 66 of their papers we have counts for
42 papers · 1 filter
Semidefinite Relaxations for Collision-Free Motion Planning
Bernhard Paus Graesdal, Alexandre Amice, Pablo A. Parrilo +1
We study semidefinite relaxations for collision-free motion planning. We focus on a point robot moving from start to goal through spherical obstacles in , subject to…
OpenVLA: An Open-Source Vision-Language-Action Model
Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti +15
Large policies pretrained on a combination of Internet-scale vision-language data and diverse robot demonstrations have the potential to change how we teach robots new skills: rath…
Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots
Cheng Chi, Zhenjia Xu, Chuer Pan +5
We present Universal Manipulation Interface (UMI) -- a data collection and policy learning framework that allows direct skill transfer from in-the-wild human demonstrations to depl…
Towards Tight Convex Relaxations for Contact-Rich Manipulation
Bernhard Paus Graesdal, Shao Yuan Chew Chia, Tobia Marcucci +4
We present a novel method for global motion planning of robotic systems that interact with the environment through contacts. Our method directly handles the hybrid nature of such t…
Certifying Bimanual RRT Motion Plans in a Second
Alexandre Amice, Peter Werner, Russ Tedrake
We present an efficient method for certifying non-collision for piecewise-polynomial motion plans in algebraic reparametrizations of configuration space. Such motion plans include…
Approximating Robot Configuration Spaces with few Convex Sets using Clique Covers of Visibility Graphs
Peter Werner, Alexandre Amice, Tobia Marcucci +2
Many computations in robotics can be dramatically accelerated if the robot configuration space is described as a collection of simple sets. For example, recently developed motion p…
Robot Fleet Learning via Policy Merging
Lirui Wang, Kaiqing Zhang, Allan Zhou +2
Fleets of robots ingest massive amounts of heterogeneous streaming data silos generated by interacting with their environments, far more than what can be stored or transmitted with…
Constrained Bimanual Planning with Analytic Inverse Kinematics
Thomas Cohn, Seiji Shaw, Max Simchowitz +1
In order for a bimanual robot to manipulate an object that is held by both hands, it must construct motion plans such that the transformation between its end effectors remains fixe…
Proximity and Visuotactile Point Cloud Fusion for Contact Patches in Extreme Deformation
Jessica Yin, Paarth Shah, Naveen Kuppuswamy +5
Visuotactile sensors are a popular tactile sensing strategy due to high-fidelity estimates of local object geometry. However, existing algorithms for processing raw sensor inputs t…
Non-Euclidean Motion Planning with Graphs of Geodesically-Convex Sets
Thomas Cohn, Mark Petersen, Max Simchowitz +1
Computing optimal, collision-free trajectories for high-dimensional systems is a challenging problem. Sampling-based planners struggle with the dimensionality, whereas trajectory o…
Fast Path Planning Through Large Collections of Safe Boxes
Tobia Marcucci, Parth Nobel, Russ Tedrake +1
We present a fast algorithm for the design of smooth paths (or trajectories) that are constrained to lie in a collection of axis-aligned boxes. We consider the case where the numbe…
Approximate Optimal Controller Synthesis for Cart-Poles and Quadrotors via Sums-of-Squares
Lujie Yang, Hongkai Dai, Alexandre Amice +1
Sums-of-squares (SOS) optimization is a promising tool to synthesize certifiable controllers for nonlinear dynamical systems. Building upon prior works, we demonstrate that SOS can…
Synthesizing Stable Reduced-Order Visuomotor Policies for Nonlinear Systems via Sums-of-Squares Optimization
Glen Chou, Russ Tedrake
We present a method for synthesizing dynamic, reduced-order output-feedback polynomial control policies for control-affine nonlinear systems which guarantees runtime stability to a…
Growing Convex Collision-Free Regions in Configuration Space using Nonlinear Programming
Mark Petersen, Russ Tedrake
One of the most difficult parts of motion planning in configuration space is ensuring a trajectory does not collide with task-space obstacles in the environment. Generating regions…
Certified Polyhedral Decompositions of Collision-Free Configuration Space
Hongkai Dai, Alexandre Amice, Peter Werner +2
Understanding the geometry of collision-free configuration space (C-free) in the presence of task-space obstacles is an essential ingredient for collision-free motion planning. Whi…
Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-dynamic Contact Models
Tao Pang, H. J. Terry Suh, Lujie Yang +1
The empirical success of Reinforcement Learning (RL) in the setting of contact-rich manipulation leaves much to be understood from a model-based perspective, where the key difficul…
Motion Planning around Obstacles with Convex Optimization
Tobia Marcucci, Mark Petersen, David von Wrangel +1
Trajectory optimization offers mature tools for motion planning in high-dimensional spaces under dynamic constraints. However, when facing complex configuration spaces, cluttered w…
Finding and Optimizing Certified, Collision-Free Regions in Configuration Space for Robot Manipulators
Alexandre Amice, Hongkai Dai, Peter Werner +2
Configuration space (C-space) has played a central role in collision-free motion planning, particularly for robot manipulators. While it is possible to check for collisions at a po…
SEED: Series Elastic End Effectors in 6D for Visuotactile Tool Use
H. J. Terry Suh, Naveen Kuppuswamy, Tao Pang +3
We propose the framework of Series Elastic End Effectors in 6D (SEED), which combines a spatially compliant element with visuotactile sensing to grasp and manipulate tools in the w…
Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning
Danny Driess, Jung-Su Ha, Marc Toussaint +1
This work proposes an optimization-based manipulation planning framework where the objectives are learned functionals of signed-distance fields that represent objects in the scene.…
Lyapunov-stable neural-network control
Hongkai Dai, Benoit Landry, Lujie Yang +2
Deep learning has had a far reaching impact in robotics. Specifically, deep reinforcement learning algorithms have been highly effective in synthesizing neural-network controllers…
Bundled Gradients through Contact via Randomized Smoothing
H. J. Terry Suh, Tao Pang, Russ Tedrake
The empirical success of derivative-free methods in reinforcement learning for planning through contact seems at odds with the perceived fragility of classical gradient-based optim…
Variable compliance and geometry regulation of Soft-Bubble grippers with active pressure control
Sihah Joonhigh, Naveen Kuppuswamy, Andrew Beaulieu +2
While compliant grippers have become increasingly commonplace in robot manipulation, finding the right stiffness and geometry for grasping the widest variety of objects remains a k…
kPAM 2.0: Feedback Control for Category-Level Robotic Manipulation
Wei Gao, Russ Tedrake
In this paper, we explore generalizable, perception-to-action robotic manipulation for precise, contact-rich tasks. In particular, we contribute a framework for closed-loop robotic…