Author name cluster

Yuchen Mo

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7 papers

2 author rows

ICRA Conference 2025 Conference Paper

DoorBot: Closed-Loop Task Planning and Manipulation for Door Opening in the Wild with Haptic Feedback

Zhi Wang
Yuchen Mo
Shengmiao Jin
Wenzhen Yuan 0001

Robots operating in unstructured environments face significant challenges when interacting with everyday objects like doors. They particularly struggle to generalize across diverse door types and conditions. Existing vision-based and open-loop planning methods often lack the robustness to handle varying door designs, mechanisms, and push/pull configurations. In this work, we propose a haptic-aware closed-loop hierarchical control framework that enables robots to explore and open different unseen doors in the wild. Our approach leverages real-time haptic feedback, allowing the robot to adjust its strategy dynamically based on force feedback during manipulation. We test our system on 20 unseen doors across different buildings, featuring diverse appearances and mechanical types. Our framework achieves a 90% success rate, demonstrating its ability to generalize and robustly handle varied door-opening tasks. This scalable solution offers potential applications in broader open-world articulated object manipulation tasks.

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ICRA Conference 2025 Conference Paper

Learning to Double Guess: An Active Perception Approach for Estimating the Center of Mass of Arbitrary Objects

Shengmiao Jin
Yuchen Mo
Wenzhen Yuan 0001

Manipulating arbitrary objects in unstructured environments is a significant challenge in robotics, primarily due to difficulties in determining an object's center of mass. This paper introduces U-GRAPH: Uncertainty-Guided Rotational Active Perception with Haptics, a novel framework to enhance the center of mass estimation using active perception. Traditional methods often rely on single interaction and are limited by the inherent inaccuracies of Force-Torque (F/T) sensors. Our approach circumvents these limitations by integrating a Bayesian Neural Network (BNN) to quantify uncertainty and guide the robotic system through multiple, information-rich interactions via grid search and a neural network that scores each action. We demonstrate the remarkable generalizability and transferability of our method with training on a small dataset with limited variation yet still perform well on unseen complex real-world objects.

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ICLR Conference 2025 Conference Paper

Sensor-Invariant Tactile Representation

Harsh Gupta
Yuchen Mo
Shengmiao Jin
Wenzhen Yuan 0001

High-resolution tactile sensors have become critical for embodied perception and robotic manipulation. However, a key challenge in the field is the lack of transferability between sensors due to design and manufacturing variations, which result in significant differences in tactile signals. This limitation hinders the ability to transfer models or knowledge learned from one sensor to another. To address this, we introduce a novel method for extracting Sensor-Invariant Tactile Representations (SITR), enabling zero-shot transfer across optical tactile sensors. Our approach utilizes a transformer-based architecture trained on a diverse dataset of simulated sensor designs, allowing it to generalize to new sensors in the real world with minimal calibration. Experimental results demonstrate the method’s effectiveness across various tactile sensing applications, facilitating data and model transferability for future advancements in the field.

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ICRA Conference 2023 Conference Paper

Towards Open-World Interactive Disambiguation for Robotic Grasping

Yuchen Mo
Hanbo Zhang
Tao Kong

Language-based communications are essential in human-robot interaction, especially for the majority of non-expert users. In this paper, we present SeeAsk, an open-world interactive visual grounding system to grasp specified targets with ambiguous natural language instructions. The main contribution of SeeAsk is that it can robustly handle open-world scenes in terms of both open-set objects and open-vocabulary interactions. Specifically, our SeeAsk is built upon modern large-scale vision-language pre-trained models and traditional decision-making process, and shows promising results to be deployed in real-world scenarios. SeeAsk outperforms previous state-of-the-art algorithms with a clear margin in terms of not only success rate but also asking smarter and more informative questions. User studies also demonstrate its advantages over previous works.

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NeurIPS Conference 2019 Conference Paper

Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach

Peilin Zhong
Yuchen Mo
Chang Xiao
Pengyu Chen
Changxi Zheng

Many generative models have to combat missing modes. The conventional wisdom to this end is by reducing through training a statistical distance (such as f -divergence) between the generated distribution and provided data distribution. But this is more of a heuristic than a guarantee. The statistical distance measures a global, but not local, similarity between two distributions. Even if it is small, it does not imply a plausible mode coverage. Rethinking this problem from a game-theoretic perspective, we show that a complete mode coverage is firmly attainable. If a generative model can approximate a data distribution moderately well under a global statistical distance measure, then we will be able to find a mixture of generators that collectively covers every data point and thus every mode, with a lower-bounded generation probability. Constructing the generator mixture has a connection to the multiplicative weights update rule, upon which we propose our algorithm. We prove that our algorithm guarantees complete mode coverage. And our experiments on real and synthetic datasets confirm better mode coverage over recent approaches, ones that also use generator mixtures but rely on global statistical distances.

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ICRA Conference 2018 Conference Paper

Active Clothing Material Perception Using Tactile Sensing and Deep Learning

Wenzhen Yuan 0001
Yuchen Mo
Shaoxiong Wang
Edward H. Adelson

Humans represent and discriminate the objects in the same category using their properties, and an intelligent robot should be able to do the same. In this paper, we build a robot system that can autonomously perceive the object properties through touch. We work on the common object category of clothing. The robot moves under the guidance of an external Kinect sensor, and squeezes the clothes with a GelSight tactile sensor, then it recognizes the 11 properties of the clothing according to the tactile data. Those properties include the physical properties, like thickness, fuzziness, softness and durability, and semantic properties, like wearing season and preferred washing methods. We collect a dataset of 153 varied pieces of clothes, and conduct 6616 robot exploring iterations on them. To extract the useful information from the high-dimensional sensory output, we applied Convolutional Neural Networks (CNN) on the tactile data for recognizing the clothing properties, and on the Kinect depth images for selecting exploration locations. Experiments show that using the trained neural networks, the robot can autonomously explore the unknown clothes and learn their properties. This work proposes a new framework for active tactile perception system with vision-touch system, and has potential to enable robots to help humans with varied clothing related housework.

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AAAI Conference 2017 Conference Paper

StructInf: Mining Structural Influence from Social Streams

Jing Zhang
Jie Tang
Yuanyi Zhong
Yuchen Mo
Juanzi Li
Guojie Song
Wendy Hall
Jimeng Sun

Social inﬂuence is a fundamental issue in social network analysis and has attracted tremendous attention with the rapid growth of online social networks. However, existing research mainly focuses on studying peer inﬂuence. This paper introduces a novel notion of structural inﬂuence and studies how to efﬁciently discover structural inﬂuence patterns from social streams. We present three sampling algorithms with theoretical unbiased guarantee to speed up the discovery process. Experiments on a big microblogging dataset show that the proposed sampling algorithms can achieve a 10× speedup compared to the exact inﬂuence pattern mining algorithm, with an average error rate of only 1. 0%. The extracted structural inﬂuence patterns have many applications. We apply them to predict retweet behavior, with performance being signiﬁcantly improved.

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