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Tengyu Liu

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6

IROS Conference 2025 Conference Paper

Ag2x2: Robust Agent-Agnostic Visual Representations for Zero-Shot Bimanual Manipulation

  • Ziyin Xiong
  • Yinghan Chen
  • Puhao Li
  • Yixin Zhu 0001
  • Tengyu Liu
  • Siyuan Huang 0001

Bimanual manipulation, fundamental to human daily activities, remains a challenging task due to its inherent complexity of coordinated control. Recent advances have enabled zero-shot learning of single-arm manipulation skills through agent-agnostic visual representations derived from human videos; however, these methods overlook crucial agentspecific information necessary for bimanual coordination, such as end-effector positions. We propose Ag2x2, a computational framework for bimanual manipulation through coordination-aware visual representations that jointly encode object states and hand motion patterns while maintaining agent-agnosticism. Extensive experiments demonstrate that Ag2x2 achieves a 73. 5% success rate across 13 diverse bimanual tasks from Bi-DexHands and PerAct 2, including challenging scenarios with deformable objects like ropes. This performance outperforms baseline methods and even surpasses the success rate of policies trained with expert-engineered rewards. Furthermore, we show that representations learned through Ag2x2 can be effectively leveraged for imitation learning, establishing a scalable pipeline for skill acquisition without expert supervision. By maintaining robust performance across diverse tasks without human demonstrations or engineered rewards, Ag2x2 represents a step toward scalable learning of complex bimanual robotic skills.

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NeurIPS Conference 2025 Conference Paper

Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation

  • Yuyang Li
  • Wenxin Du
  • Chang Yu
  • Puhao Li
  • Zihang Zhao
  • Tengyu Liu
  • Chenfanfu Jiang
  • Yixin Zhu

Tactile sensing is crucial for achieving human-level robotic capabilities in manipulation tasks. As a promising solution, Vision-based Tactile Sensors (VBTSs) offer high spatial resolution and cost-effectiveness, but present unique challenges in robotics for their complex physical characteristics and visual signal processing requirements. The lack of efficient and accurate simulation tools for VBTSs has significantly limited the scale and scope of tactile robotics research. We present Taccel, a high-performance simulation platform that integrates Incremental Potential Contact (IPC) and Affine Body Dynamics (ABD) to model robots, tactile sensors, and objects with both accuracy and unprecedented speed, achieving a total of 915 FPS with 4096 parallel environments. Unlike previous simulators that operate at sub-real-time speeds with limited parallelization, Taccel provides precise physics simulation and realistic tactile signals while supporting flexible robot-sensor configurations through user-friendly APIs. Through extensive validation in object recognition, robotic grasping, and articulated object manipulation, we demonstrate precise simulation and successful sim-to-real transfer. These capabilities position Taccel as a powerful tool for scaling up tactile robotics research and development, potentially transforming how robots interact with and understand their physical environment.

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IROS Conference 2024 Conference Paper

Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations

  • Puhao Li
  • Tengyu Liu
  • Yuyang Li
  • Muzhi Han
  • Haoran Geng
  • Shu Wang 0002
  • Yixin Zhu 0001
  • Song-Chun Zhu

Autonomous robotic systems capable of learning novel manipulation tasks are poised to transform industries from manufacturing to service automation. However, current methods (e. g. , VIP and R3M) still face significant hurdles, notably the domain gap among robotic embodiments and the sparsity of successful task executions within specific action spaces, resulting in misaligned and ambiguous task representations. We introduce Ag2Manip (Agent-Agnostic representations for Manipulation), a framework aimed at addressing these challenges through two key innovations: (1) an agent-agnostic visual representation derived from human manipulation videos, with the specifics of embodiments obscured to enhance generalizability; and (2) an agent-agnostic action representation abstracting a robot’s kinematics to a universal agent proxy, emphasizing crucial interactions between end-effector and object. Ag2Manip has been empirically validated across simulated benchmarks, showing a 325% performance increase without relying on domain-specific demonstrations. Ablation studies further underline the essential contributions of the agent-agnostic visual and action representations to this success. Extending our evaluations to the real world, Ag2Manip significantly improves imitation learning success rates from 50% to 77. 5%, demonstrating its effectiveness and generalizability across both simulated and real environments.

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ICRA Conference 2023 Conference Paper

DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation

  • Ruicheng Wang
  • Jialiang Zhang
  • Jiayi Chen 0003
  • Yinzhen Xu
  • Puhao Li
  • Tengyu Liu
  • He Wang 0010

Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology. However, dexterous grasping is much more under-explored than object grasping with parallel grippers, partially due to the lack of a large-scale dataset. In this work, we present a large-scale robotic dexterous grasp dataset, DexGraspNet, generated by our proposed highly efficient synthesis method that can be generally applied to any dexterous hand. Our method leverages a deeply accelerated differentiable force closure estimator and thus can efficiently and robustly synthesize stable and diverse grasps on a large scale. We choose ShadowHand and generate 1. 32 million grasps for 5355 objects, covering more than 133 object categories and containing more than 200 diverse grasps for each object instance, with all grasps having been validated by the Isaac Gym simulator. Compared to the previous dataset from Liu et al. generated by GraspIt! , our dataset has not only more objects and grasps, but also higher diversity and quality. Via performing cross-dataset experiments, we show that training several algorithms of dexterous grasp synthesis on our dataset significantly outperforms training on the previous one. To access our data and code, including code for human and Allegro grasp synthesis, please visit our project page: https://pku-epic.github.io/DexGraspNet/.

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ICRA Conference 2023 Conference Paper

GenDexGrasp: Generalizable Dexterous Grasping

  • Puhao Li
  • Tengyu Liu
  • Yuyang Li
  • Yiran Geng
  • Yixin Zhu 0001
  • Yaodong Yang 0001
  • Siyuan Huang 0001

Generating dexterous grasping has been a long-standing and challenging robotic task. Despite recent progress, existing methods primarily suffer from two issues. First, most prior art focuses on a specific type of robot hand, lacking generalizable capability of handling unseen ones. Second, prior arts oftentimes fail to rapidly generate diverse grasps with a high success rate. To jointly tackle these challenges with a unified solution, we propose the GenDexGrasp, a novel hand-agnostic grasping algorithm for generalizable grasping. GenDexGrasp is trained on our proposed large-scale multi-hand grasping dataset MultiDex synthesized with force closure optimization. By leveraging the contact map as a hand-agnostic intermediate representation, GenDexGrasp efficiently generates diverse and plausible grasping poses with a high success rate and can transfer among diverse multi-fingered robotic hands. Compared with previous methods, GenDexGrasp achieves a three-way trade-off among success rate, inference speed, and diversity.

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NeurIPS Conference 2022 Conference Paper

HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes

  • Zan Wang
  • Yixin Chen
  • Tengyu Liu
  • Yixin Zhu
  • Wei Liang
  • Siyuan Huang

Learning to generate diverse scene-aware and goal-oriented human motions in 3D scenes remains challenging due to the mediocre characters of the existing datasets on Human-Scene Interaction (HSI); they only have limited scale/quality and lack semantics. To fill in the gap, we propose a large-scale and semantic-rich synthetic HSI dataset, denoted as HUMANISE, by aligning the captured human motion sequences with various 3D indoor scenes. We automatically annotate the aligned motions with language descriptions that depict the action and the individual interacting objects; e. g. , sit on the armchair near the desk. HUMANIZE thus enables a new generation task, language-conditioned human motion generation in 3D scenes. The proposed task is challenging as it requires joint modeling of the 3D scene, human motion, and natural language. To tackle this task, we present a novel scene-and-language conditioned generative model that can produce 3D human motions of the desirable action interacting with the specified objects. Our experiments demonstrate that our model generates diverse and semantically consistent human motions in 3D scenes.

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