activity
20182022
most citedRiemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping

10 citations · 34 across the 9 of their papers we have counts for

collaborators

19 papers

cs.RO20226 cited

MidasTouch: Monte-Carlo inference over distributions across sliding touch

Sudharshan Suresh, Zilin Si, Stuart Anderson +2

We present MidasTouch, a tactile perception system for online global localization of a vision-based touch sensor sliding on an object surface. This framework takes in posed tactile…

cs.RO20224 cited

In-Hand Gravitational Pivoting Using Tactile Sensing

Jason Toskov, Rhys Newbury, Mustafa Mukadam +2

We study gravitational pivoting, a constrained version of in-hand manipulation, where we aim to control the rotation of an object around the grip point of a parallel gripper. To ac…

cs.RO2022

iSDF: Real-Time Neural Signed Distance Fields for Robot Perception

Joseph Ortiz, Alexander Clegg, Jing Dong +4

We present iSDF, a continual learning system for real-time signed distance field (SDF) reconstruction. Given a stream of posed depth images from a moving camera, it trains a random…

cs.RO2021

A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation

Bernardo Aceituno, Alberto Rodriguez, Shubham Tulsiani +2

Specifying tasks with videos is a powerful technique towards acquiring novel and general robot skills. However, reasoning over mechanics and dexterous interactions can make it chal…

cs.CV2021

Revitalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation

Taosha Fan, Kalyan Vasudev Alwala, Donglai Xiang +3

We propose a novel sparse constrained formulation and from it derive a real-time optimization method for 3D human pose and shape estimation. Our optimization method, SCOPE (Sparse…

cs.CV2021

Where2Act: From Pixels to Actions for Articulated 3D Objects

Kaichun Mo, Leonidas Guibas, Mustafa Mukadam +2

One of the fundamental goals of visual perception is to allow agents to meaningfully interact with their environment. In this paper, we take a step towards that long-term goal -- w…