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Junchen Liu

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4 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Cameras as Relative Positional Encoding

Ruilong Li
Brent Yi
Junchen Liu
Hang Gao
Yi Ma
Angjoo Kanazawa

Transformers are increasingly prevalent for multiview computer vision tasks, where geometric relationships between viewpoints are critical for 3D perception. To leverage these relationships, multiview transformers must use camera geometry to ground visual tokens in 3D space. In this work, we compare techniques for conditioning transformers on cameras: token-level raymap encodings, attention-level relative pose encodings, and a new relative encoding we propose—Projective Positional Encoding (PRoPE)—that captures complete camera frustums, both intrinsics and extrinsics, as a relative positional encoding. Our experiments begin by showing how relative conditioning methods improve performance in feedforward novel view synthesis, with further gains from PRoPE. This holds across settings: scenes with both shared and varying intrinsics, when combining token- and attention-level conditioning, and for generalization to inputs with out-of-distribution sequence lengths and camera intrinsics. We then verify that these benefits persist for different tasks, stereo depth estimation and discriminative spatial cognition, as well as larger model sizes.

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IROS Conference 2025 Conference Paper

Using Upper Limb Carrying Exoskeleton with Dual-Model Torque Control Strategy to Reduce Load Impact

Daming Liu
Ye Li
Junchen Liu
Ziqi Wang
Jie Zhao 0003
Yanhe Zhu

Exoskeleton technology holds significant promise within the human-centric paradigm of Industry 5. 0 for mitigating work-related musculoskeletal disorders (WMSDs). However, existing systems often struggle with mismatched assistive torque and inefficient human-machine collaboration under dynamic loading conditions, largely due to insufficient motion intent recognition accuracy. This study proposes a dual-model-based multimodal fusion control strategy that integrates a bidirectional LSTM neural network (Bi-LSTM) with a transformer-based multi-task learning model (MTL) to enable real-time torque compensation and accurate prediction of dynamic load mass under varying conditions. The team developed a lightweight elbow joint exoskeleton prototype, leveraging multi-modal information to enhance assistive torque prediction accuracy. Experimental results show an 83. 7% reduction in agonist muscle activation under a 3. 5 kg load compared to conditions without the exoskeleton, underscoring its potential for industrial material handling scenarios.

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ICRA Conference 2024 Conference Paper

Human-Exoskeleton Locomotion Interaction Experience Transfer: Speeding up and Improving the Performance of Preference-based Optimizations of Exoskeleton Assistance During Walking

Hongwu Li
Junchen Liu
Ziqi Wang
Haotian Ju
Tianjiao Zheng
Yongsheng Gao 0002
Jie Zhao 0003
Yanhe Zhu

Preference-based optimizing methods have shown their advantages and potential in exploring individual, comfortable, and effective control strategies and assistance parameters of exoskeletons during locomotion. Research indicates that compared with naive wearers, knowledgeable wearers with abundant exoskeleton assistance experience have obvious advantages in speeding up the parameters exploration process and improving the assistant performance. However, there is no existing method that could utilize the human-exoskeleton locomotion interaction experience (HELIE) to assist naive wearers during the exploration process. In this work, we propose a novel preference-based human-exoskeleton locomotion interaction experience transfer (LIET) framework, which could speed up the exploration of human-preferred parameters and acquire more satisfying results for naive wearers via the HELIE acquired from knowledgeable wearers. In addition, based on the proposed LIET framework, we establish the mathematical expression of the HELIE transfer during exoskeleton assistance. This will promote the research that concerns utilizing HELIE for exoskeleton control parameters optimizations in the future. Finally, experiments demonstrate the proposed LIET framework could speed up the exploration process and acquire more satisfying optimized results for naive wearers.

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IROS Conference 2024 Conference Paper

Using Hip Assisted Running Exoskeleton with Impact Isolation Mechanism to Improve Energy Efficiency

Ziqi Wang
Junchen Liu
Hongwu Li
Qinghua Zhang
Xianglong Li
Yi Huang
Haotian Ju
Tianjiao Zheng

Research has indicated that exoskeletons can assist human movement, but due to the influence of additional weight and challenges in control strategy design, only a few exoskeletons effectively reduce the wearers’ metabolic costs during running. This paper proposes an innovative and efficient hip-assisted running exoskeleton (HARE) designed to facilitate the flexion and extension movements of the joint along the sagittal plane. In the field of structural engineering, we propose implementing an active-passive combination constant force suspension system, hereinafter referred to as CFS, to effectively mitigate the impact of inertial forces during running. The decoupled transmission mechanism allows the CFS and assist mechanisms to operate independently, ensuring the tension of the cables. The flexible structural design can reduce the locomotion limitation on human bodies and reduce the additional energy burden on the body. In control strategy designing, the joint torque-generating strategy provides personalized assistance strategies for wearers to actively optimize the control parameters. Meanwhile, the safety control strategy based on abnormal gait recognition can ensure human safety. Experiments have shown that compared to not wearing exoskeletons, this device can reduce the energy consumption of the human body by 5. 33 % at a speed of 9 km/h. This demonstrates its potential in human motion assistance processes.

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