Zipeng Fu
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I am a final-year PhD researcher in computer science at Stanford AI Lab, advised by Chelsea Finn.
I was a researcher at Google DeepMind, working with Jie Tan.
My research is supported by Pierre and Christine Lamond Fellowship.
Previously, I was a master's student in the Machine Learning Department and a student researcher in the Robotics Institute at CMU, advised by Deepak Pathak and Jitendra Malik.
I completed my bachelor's in Computer Science and Applied Math at UCLA, advised by Song-Chun Zhu.
My research interests lie in the intersection of Robotics, Machine Learning and Computer Vision. I care about robust robot performance and deployable robot systems in the unstructured open world.
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HumanPlus: Humanoid Shadowing and Imitation from Humans
Zipeng Fu*, Qingqing Zhao*, Qi Wu*, Gordon Wetzstein, Chelsea Finn
CoRL 2024
Best Paper Award Finalist (top 6)
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One of the key arguments for building robots that have similar form factors to human beings is that we can leverage the massive human data for training. Yet, doing so has remained challenging in practice due to the complexities in humanoid perception and control, lingering physical gaps between humanoids and humans in morphologies and actuation, and lack of a data pipeline for humanoids to learn autonomous skills from egocentric vision. In this paper, we introduce a full-stack system for humanoids to learn motion and autonomous skills from human data. We first train a low-level policy in simulation via reinforcement learning using existing 40-hour human motion datasets. This policy transfers to the real world and allows humanoid robots to follow human body and hand motion in real time using only a RGB camera, i.e. shadowing. Through shadowing, human operators can teleoperate humanoids to collect whole-body data for learning different tasks in the real world. Using the data collected, we then perform supervised behavior cloning to train skill policies using egocentric vision, allowing humanoids to complete different tasks autonomously by imitating human skills. We demonstrate the system on our customized 33-DoF 180cm humanoid, autonomously completing tasks such as wearing a shoe to stand up and walk, unloading objects from warehouse racks, folding a sweatshirt, rearranging objects, typing, and greeting another robot with 60-100% success rates using up to 40 demonstrations.
@inproceedings{fu2024humanplus,
author = {Fu, Zipeng and Zhao, Qingqing
and Wu, Qi and Wetzstein, Gordon
and Finn, Chelsea},
title = {HumanPlus: Humanoid Shadowing
and Imitation from Humans},
booktitle = {Conference on Robot Learning ({CoRL})},
year = {2024}
}
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Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
Zipeng Fu*, Tony Z. Zhao*, Chelsea Finn
CoRL 2024
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Imitation learning from human demonstrations has shown impressive performance in robotics. However, most results focus on table-top manipulation, lacking the mobility and dexterity necessary for generally useful tasks. In this work, we develop a system for imitating mobile manipulation tasks that are bimanual and require whole-body control. We first present Mobile ALOHA, a low-cost and whole-body teleoperation system for data collection. It augments the ALOHA system with a mobile base, and a whole-body teleoperation interface. Using data collected with Mobile ALOHA, we then perform supervised behavior cloning and find that co-training with existing static ALOHA datasets boosts performance on mobile manipulation tasks. With 50 demonstrations for each task, co-training can increase success rates by up to 90%, allowing Mobile ALOHA to autonomously complete complex mobile manipulation tasks such as sauteing and serving a piece of shrimp, opening a two-door wall cabinet to store heavy cooking pots, calling and entering an elevator, and lightly rinsing a used pan using a kitchen faucet.
@inproceedings{fu2024mobile,
author = {Fu, Zipeng and
Zhao, Tony Z. and Finn, Chelsea},
title = {Mobile ALOHA: Learning Bimanual Mobile Manipulation
with Low-Cost Whole-Body Teleoperation},
booktitle = {Conference on Robot Learning ({CoRL})},
year = {2024}
}
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Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs
Hao-Tien Lewis Chiang*, Zhuo Xu*, Zipeng Fu*, Mithun George Jacob, Tingnan Zhang, Tsang-Wei Edward Lee, Wenhao Yu, Connor Schenck, David Rendleman, Dhruv Shah, Fei Xia, Jasmine Hsu, Jonathan Hoech, Pete Florence, Sean Kirmani, Sumeet Singh, Vikas Sindhwani, Carolina Parada*, Chelsea Finn*, Peng Xu*, Sergey Levine*, Jie Tan*
CoRL 2024
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An elusive goal in navigation research is to build an intelligent agent that can understand multimodal instructions including natural language and image, and perform useful navigation. To achieve this, we study a widely useful category of navigation tasks we call Multimodal Instruction Navigation with demonstration Tours (MINT), in which the environment prior is provided through a previously recorded demonstration video. Recent advances in Vision Language Models (VLMs) have shown a promising path in achieving this goal as it demonstrates capabilities in perceiving and reasoning about multimodal inputs. However, VLMs are typically trained to predict textual output and it is an open research question about how to best utilize them in navigation. To solve MINT, we present Mobility VLA, a hierarchical Vision-Language-Action (VLA) navigation policy that combines the environment understanding and common sense reasoning power of long-context VLMs and a robust low-level navigation policy based on topological graphs. The high-level policy consists of a long-context VLM that takes the demonstration tour video and the multimodal user instruction as input to find the goal frame in the tour video. Next, a low-level policy uses the goal frame and an offline constructed topological graph to generate robot actions at every timestep. We evaluated Mobility VLA in a 836m^2 real world environment and show that Mobility VLA has a high end-to-end success rates on previously unsolved multimodal instructions such as "Where should I return this?" while holding a plastic bin.
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UMI on Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers
Huy Ha*, Yihuai Gao*, Zipeng Fu, Jie Tan, Shuran Song
CoRL 2024
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We introduce UMI-on-Legs, a new framework that combines real-world and simulation data for quadruped manipulation systems. We scale task-centric data collection in the real world using a hand-held gripper (UMI), providing a cheap way to demonstrate task-relevant manipulation skills without a robot. Simultaneously, we scale robot-centric data in simulation by training whole-body controller for task-tracking without task simulation setups. The interface between these two policies is end-effector trajectories in the task frame, inferred by the manipulation policy and passed to the whole-body controller for tracking. We evaluate UMI-on-Legs on prehensile, non-prehensile, and dynamic manipulation tasks, and report over 70% success rate on all tasks. Lastly, we demonstrate the zero-shot cross-embodiment deployment of a pre-trained manipulation policy checkpoint from prior work, originally intended for a fixed-base robot arm, on our quadruped system. We believe this framework provides a scalable path towards learning expressive manipulation skills on dynamic robot embodiments.
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Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Open X-Embodiment Collaboration led by Google DeepMind
ICRA 2024
Best Paper Award
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Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train “generalist” X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms.
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Robot Parkour Learning
Ziwen Zhuang*, Zipeng Fu*, Jianren Wang, Chris Atkeson, Sören Schwertfeger, Chelsea Finn, Hang Zhao
CoRL 2023 (Oral)
Best Systems Paper Award Finalist (top 3)
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Parkour is a grand challenge for legged locomotion that requires robots to overcome various obstacles rapidly in complex environments. Existing methods can generate either diverse but blind locomotion skills or vision-based but specialized skills by using reference animal data or complex rewards. However, autonomous parkour requires robots to learn generalizable skills that are both vision-based and diverse to perceive and react to various scenarios. In this work, we propose a system for learning a single end-to-end vision-based parkour policy of diverse parkour skills using a simple reward without any reference motion data. We develop a reinforcement learning method inspired by direct collocation to generate parkour skills, including climbing over high obstacles, leaping over large gaps, crawling beneath low barriers, squeezing through thin slits, and running. We distill these skills into a single vision-based parkour policy and transfer it to a quadrupedal robot using its egocentric depth camera. We demonstrate that our system can empower two different low-cost robots to autonomously select and execute appropriate parkour skills to traverse challenging real-world environments.
@inproceedings{zhuang2023robot,
author = {Zhuang, Ziwen and Fu, Zipeng and
Wang, Jianren and Atkeson, Christopher and
Schwertfeger, Sören and Finn, Chelsea and
Zhao, Hang},
title = {Robot Parkour Learning},
booktitle = {Conference on Robot Learning ({CoRL})},
year = {2023}
}
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Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion
Zipeng Fu*, Xuxin Cheng*, Deepak Pathak
CoRL 2022 (Oral)
Best Systems Paper Award Finalist (top 4)
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An attached arm can significantly increase the applicability of legged robots to several mobile manipulation tasks that are not possible for the wheeled or tracked counterparts. The standard control pipeline for such legged manipulators is to decouple the controller into that of manipulation and locomotion. However, this is ineffective and requires immense engineering to support coordination between the arm and legs, error can propagate across modules causing non-smooth unnatural motions. It is also biological implausible where there is evidence for strong motor synergies across limbs. In this work, we propose to learn a unified policy for whole-body control of a legged manipulator using reinforcement learning. We propose Regularized Online Adaptation to bridge the Sim2Real gap for high-DoF control, and Advantage Mixing exploiting the causal dependency in the action space to overcome local minima during training the whole-body system. We also present a simple design for a low-cost legged manipulator, and find that our unified policy can demonstrate dynamic and agile behaviors across several task setups.
@inproceedings{fu2022deep,
author = {Fu, Zipeng and Cheng, Xuxin and
Pathak, Deepak},
title = {Deep Whole-Body Control: Learning a Unified Policy
for Manipulation and Locomotion},
booktitle = {Conference on Robot Learning ({CoRL})},
year = {2022}
}
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Coupling Vision and Proprioception for Navigation of Legged Robots
Zipeng Fu*, Ashish Kumar*, Ananye Agarwal, Haozhi Qi, Jitendra Malik, Deepak Pathak
CVPR 2022
Best Paper at Multimodal Learning Workshop
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We exploit the complementary strengths of vision and proprioception to develop a point-goal navigation system for legged robots, called VP-Nav. Legged systems are capable of traversing more complex terrain than wheeled robots, but to fully utilize this capability, we need a high-level path planner in the navigation system to be aware of the walking capabilities of the low-level locomotion policy in varying environments. We achieve this by using proprioceptive feedback to ensure the safety of the planned path by sensing unexpected obstacles like glass walls, terrain properties like slipperiness or softness of the ground and robot properties like extra payload that are likely missed by vision. The navigation system uses onboard cameras to generate an occupancy map and a corresponding cost map to reach the goal. A fast marching planner then generates a target path. A velocity command generator takes this as input to generate the desired velocity for the walking policy. A safety advisor module adds sensed unexpected obstacles to the occupancy map and environment-determined speed limits to the velocity command generator. We show superior performance compared to wheeled robot baselines, and ablation studies which have disjoint high-level planning and low-level control. We also show the real-world deployment of VP-Nav on a quadruped robot with onboard sensors and computation.
@inproceedings{fu2021coupling,
author = {Fu, Zipeng and Kumar, Ashish and
Agarwal, Ananye and Qi, Haozhi and
Malik, Jitendra and Pathak, Deepak},
title = {Coupling Vision and Proprioception
for Navigation of Legged Robots},
booktitle = {{CVPR}},
year = {2022}
}
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Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots
Zipeng Fu, Ashish Kumar, Jitendra Malik, Deepak Pathak
CoRL 2021
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Legged locomotion is commonly studied and expressed as a discrete set of gait patterns, like walk, trot, gallop, which are usually treated as given and pre-programmed in legged robots for efficient locomotion at different speeds. However, fixing a set of pre-programmed gaits limits the generality of locomotion. Recent animal motor studies show that these conventional gaits are only prevalent in ideal flat terrain conditions while real-world locomotion is unstructured and more like bouts of intermittent steps. What principles could lead to both structured and unstructured patterns across mammals and how to synthesize them in robots? In this work, we take an analysis-by-synthesis approach and learn to move by minimizing mechanical energy. We demonstrate that learning to minimize energy consumption is sufficient for the emergence of natural locomotion gaits at different speeds in real quadruped robots. The emergent gaits are structured in ideal terrains and look similar to that of horses and sheep. The same approach leads to unstructured gaits in rough terrains which is consistent with the findings in animal motor control. We validate our hypothesis in both simulation and real hardware across natural terrains.
@inproceedings{fu2021minimizing,
author = {Fu, Zipeng and Kumar, Ashish and Malik, Jitendra and Pathak, Deepak},
title = {Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots},
booktitle = {Conference on Robot Learning (CoRL)},
year = {2021}
}
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RMA: Rapid Motor Adaptation for Legged Robots
Ashish Kumar, Zipeng Fu, Deepak Pathak, Jitendra Malik
RSS 2021
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Successful real-world deployment of legged robots would require them to adapt in real-time to unseen scenarios like changing terrains, changing payloads, wear and tear. This paper presents Rapid Motor Adaptation (RMA) algorithm to solve this problem of real-time online adaptation in quadruped robots. RMA consists of two components: a base policy and an adaptation module. The combination of these components enables the robot to adapt to novel situations in fractions of a second. RMA is trained completely in simulation without using any domain knowledge like reference trajectories or predefined foot trajectory generators and is deployed on the A1 robot without any fine-tuning. We train RMA on a varied terrain generator using bioenergetics-inspired rewards and deploy it on a variety of difficult terrains including rocky, slippery, deformable surfaces in environments with grass, long vegetation, concrete, pebbles, stairs, sand, etc. RMA shows state-of-the-art performance across diverse real-world as well as simulation experiments.
@inproceedings{kumar2021rma,
author = {Kumar, Ashish and Fu, Zipeng and Pathak, Deepak and Malik, Jitendra},
title = {RMA: Rapid Motor Adaptation for Legged Robots},
booktitle = {Robotics: Science and Systems (RSS)},
year = {2021}
}
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