206 citations · 340 across the 26 of their papers we have counts for
29 papers
Co-design Gym: A Unified Benchmark for Embodiment-Policy Co-optimization
Aviraj Newatia, Yordan Tsvetkov, Leonard Pleiss +2
Finding an optimal behaviour policy within a given environment is a widely studied problem in domains as diverse as games, robotics, energy infrastructure, communication networks,…
SoTa: Soft Tactile Skins for Dexterous Manipulation
Jingyun Yang, Baiyu Shi, Timothy Yu +6
A growing body of work suggests that tactile sensing gives robot policies contact information that complements vision in dexterous manipulation. However, visuo-tactile robot data r…
DOT-Sim: Differentiable Optical Tactile Simulation with Precise Real-to-Sim Physical Calibration
Yang You, Won Kyung Do, Aiden Swann +3
Simulating optical tactile sensors presents significant challenges due to their high deformability and intricate optical properties. To address these issues and enable a physically…
CUPID: Curating Data your Robot Loves with Influence Functions
Christopher Agia, Rohan Sinha, Jingyun Yang +5
In robot imitation learning, policy performance is tightly coupled with the quality and composition of the demonstration data. Yet, developing a precise understanding of how indivi…
HoMeR: Learning In-the-Wild Mobile Manipulation via Hybrid Imitation and Whole-Body Control
Priya Sundaresan, Rhea Malhotra, Phillip Miao +7
We introduce HoMeR, an imitation learning framework for mobile manipulation that combines whole-body control with hybrid action modes that handle both long-range and fine-grained m…
Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel
Carlota Parés-Morlans, Michelle Yi, Claire Chen +4
Tasks that involve complex interactions between objects with unknown dynamics make planning before execution difficult. These tasks require agents to iteratively improve their acti…
Deformable Cargo Transport in Microgravity with Astrobee
Daniel Morton, Rika Antonova, Brian Coltin +2
We present pyastrobee: a simulation environment and control stack for Astrobee in Python, with an emphasis on cargo manipulation and transport tasks. We also demonstrate preliminar…
Mobi-: Mobilizing Your Robot Learning Policy
Jingyun Yang, Isabella Huang, Brandon Vu +3
Learned visuomotor policies are capable of performing increasingly complex manipulation tasks. However, most of these policies are trained on data collected from limited robot posi…
Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress
Christopher Agia, Rohan Sinha, Jingyun Yang +4
Robot behavior policies trained via imitation learning are prone to failure under conditions that deviate from their training data. Thus, algorithms that monitor learned policies a…
EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning
Jingyun Yang, Zi-ang Cao, Congyue Deng +3
Building effective imitation learning methods that enable robots to learn from limited data and still generalize across diverse real-world environments is a long-standing problem i…
EquivAct: SIM(3)-Equivariant Visuomotor Policies beyond Rigid Object Manipulation
Jingyun Yang, Congyue Deng, Jimmy Wu +3
If a robot masters folding a kitchen towel, we would expect it to master folding a large beach towel. However, existing policy learning methods that rely on data augmentation still…
TidyBot: Personalized Robot Assistance with Large Language Models
Jimmy Wu, Rika Antonova, Adam Kan +6
For a robot to personalize physical assistance effectively, it must learn user preferences that can be generally reapplied to future scenarios. In this work, we investigate persona…
Learning Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation
Mengxi Li, Rika Antonova, Dorsa Sadigh +1
When humans perform contact-rich manipulation tasks, customized tools are often necessary to simplify the task. For instance, we use various utensils for handling food, such as kni…
In-Hand Manipulation of Unknown Objects with Tactile Sensing for Insertion
Chaoyi Pan, Marion Lepert, Shenli Yuan +2
In this paper, we present a method to manipulate unknown objects in-hand using tactile sensing without relying on a known object model. In many cases, vision-only approaches may no…
Rethinking Optimization with Differentiable Simulation from a Global Perspective
Rika Antonova, Jingyun Yang, Krishna Murthy Jatavallabhula +1
Differentiable simulation is a promising toolkit for fast gradient-based policy optimization and system identification. However, existing approaches to differentiable simulation ha…
DiffCloud: Real-to-Sim from Point Clouds with Differentiable Simulation and Rendering of Deformable Objects
Priya Sundaresan, Rika Antonova, Jeannette Bohg
Research in manipulation of deformable objects is typically conducted on a limited range of scenarios, because handling each scenario on hardware takes significant effort. Realisti…
A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation
Rika Antonova, Jingyun Yang, Priya Sundaresan +3
Deformable object manipulation remains a challenging task in robotics research. Conventional techniques for parameter inference and state estimation typically rely on a precise def…
Learning Periodic Tasks from Human Demonstrations
Jingyun Yang, Junwu Zhang, Connor Settle +3
We develop a method for learning periodic tasks from visual demonstrations. The core idea is to leverage periodicity in the policy structure to model periodic aspects of the tasks.…
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym
Rika Antonova, Fabio Ramos, Rafael Possas +1
BayesSim is a statistical technique for domain randomization in reinforcement learning based on likelihood-free inference of simulation parameters. This paper outlines BayesSimIG:…
Sequential Topological Representations for Predictive Models of Deformable Objects
Rika Antonova, Anastasiia Varava, Peiyang Shi +2
Deformable objects present a formidable challenge for robotic manipulation due to the lack of canonical low-dimensional representations and the difficulty of capturing, predicting,…
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
Rika Antonova, Maksim Maydanskiy, Danica Kragic +2
We address the problem of learning reusable state representations from streaming high-dimensional observations. This is important for areas like Reinforcement Learning (RL), which…
Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
Rika Antonova, Akshara Rai, Tianyu Li +1
Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments. In this work we focus on robotics problems with a budget of only 10-20 trials. This is a ve…
Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation
Rika Antonova, Mia Kokic, Johannes A. Stork +1
We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optim…
Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots
Akshara Rai, Rika Antonova, Franziska Meier +1
Learning for control can acquire controllers for novel robotic tasks, paving the path for autonomous agents. Such controllers can be expert-designed policies, which typically requi…