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Jialiang Zhao

10 accepted papers

2025

Fabrica: Dual-Arm Assembly of General Multi-Part Objects via Integrated Planning and Learning

CoRL 2025oral

Multi-part assembly poses significant challenges for robotic systems to execute long-horizon, contact-rich manipulation with generalization across complex geometries. We present a dual-arm robotic system capable of end-to-end planning and control for autonomous assembly of general multi-part objects…

Cited by 0SourceScholar
2025

Learning Object Compliance via Young's Modulus from Single Grasps using Camera-Based Tactile Sensors

IROS 2025

Compliance is a useful parametrization of tactile information that humans often utilize in manipulation tasks. It can be used to inform low-level contact-rich actions or characterize objects at a high-level. In robotic manipulation, existing approaches to estimate compliance have struggled to genera

Cited by 4SourcecodeScholar
2025

PolyTouch: A Robust Multi-Modal Tactile Sensor for Contact-Rich Manipulation Using Tactile-Diffusion Policies

ICRA 2025

Achieving robust dexterous manipulation in un-structured domestic environments remains a significant challenge in robotics. Even with state-of-the-art robot learning methods, haptic-oblivious control strategies (i.e. those relying only on external vision and/or proprioception) often fall short due t

Cited by 23SourceScholar
2024

PoCo: Policy Composition from and for Heterogeneous Robot Learning

RSS 2024poster

Training general robotic policies from heterogeneous data for different tasks is a significant challenge. Existing robotic datasets vary in different modalities such as color, depth, tactile, and proprioceptive information, and collected in different domains such as simulation, real robots, and huma…

Cited by 35SourcePDFScholar
2024

Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers

NeurIPS 2024spotlight

One of the roadblocks for training generalist robotic models today is heterogeneity. Previous robot learning methods often collect data to train with one specific embodiment for one task, which is expensive and prone to overfitting. This work studies the problem of learning policy representations th…

2024

Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks

CoRL 2024poster

This paper presents T3: Transferable Tactile Transformers, a framework for tactile representation learning that scales across multi-sensors and multi-tasks.T3 is designed to overcome the contemporary issue that camera-based tactile sensing is extremely heterogeneous, i.e. sensors are built into diff…

Cited by 18SourceScholar
2023

FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback

ICRA 2023poster

In this paper, we address the problem of using visuo-tactile feedback for 6-DoF localization and 3D reconstruction of unknown in-hand objects. We propose FingerSLAM, a closed-loop factor graph-based pose estimator that combines local tactile sensing at finger-tip and global vision sensing from a wri…

Cited by 19SourceScholar
2023

GelSight Svelte: A Human Finger-Shaped Single-Camera Tactile Robot Finger with Large Sensing Coverage and Proprioceptive Sensing

IROS 2023poster

Camera-based tactile sensing is a low-cost, popular approach to obtain highly detailed contact geometry information. However, most existing camera-based tactile sensors are fingertip sensors, and longer fingers often require extraneous elements to obtain an extended sensing area similar to the full…

Cited by 37SourceScholar
2020

Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation

CoRL 2020

Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e.g., sliding an object to a goal pose while maintaining contact with a table. Individual subtasks can be achieved by task-axis controllers defined relative to the objects being manipulated, and a set of object-

2020

Towards Robotic Assembly by Predicting Robust, Precise and Task-oriented Grasps

CoRL 2020

Robust task-oriented grasp planning is vital for autonomous robotic precision assembly tasks. Knowledge of the objects’ geometry and preconditions of the target task should be incorporated when determining the proper grasp to execute. However, several factors contribute to the challenges of realizin

Cited by 0SourcePDFScholar