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Jacky Liang

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14 papers

1 author row

ICRA Conference 2025 Conference Paper

Chain-of-Modality: Learning Manipulation Programs from Multimodal Human Videos with Vision-Language-Models

Chen Wang 0053
Fei Xia 0002
Wenhao Yu 0003
Tingnan Zhang
Ruohan Zhang
C. Karen Liu
Li Fei-Fei 0001
Jie Tan 0001

Learning to perform manipulation tasks from human videos is a promising approach for teaching robots. However, many manipulation tasks require changing control parameters during task execution, such as force, which visual data alone cannot capture. In this work, we leverage sensing devices such as armbands that measure human muscle activities and microphones that record sound, to capture the details in the human manipulation process, and enable robots to extract task plans and control parameters to perform the same task. To achieve this, we introduce Chain-of-Modality (CoM), a prompting strategy that enables Vision Language Models to reason about multimodal human demonstration data - videos coupled with muscle or audio signals. By progressively integrating information from each modality, CoM refines a task plan and generates detailed control parameters, enabling robots to perform manipulation tasks based on a single multimodal human video prompt. Our experiments show that CoM delivers a threefold improvement in accuracy for extracting task plans and control parameters compared to baselines, with strong generalization to new task setups and objects in real-world robot experiments. Videos and code are available at chain-of-modality.github.io

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ICLR Conference 2025 Conference Paper

Vision Language Models are In-Context Value Learners

Yecheng Jason Ma 0001
Joey Hejna
Chuyuan Fu
Dhruv Shah
Jacky Liang
Zhuo Xu
Sean Kirmani
Peng Xu 0010

Predicting temporal progress from visual trajectories is important for intelligent robots that can learn, adapt, and improve. However, learning such progress estimator, or temporal value function, across different tasks and domains requires both a large amount of diverse data and methods which can scale and generalize. To address these challenges, we present Generative Value Learning (GVL), a universal value function estimator that leverages the world knowledge embedded in vision-language models (VLMs) to predict task progress. Naively asking a VLM to predict values for a video sequence performs poorly due to the strong temporal correlation between successive frames. Instead, GVL poses value estimation as a temporal ordering problem over shuffled video frames; this seemingly more challenging task encourages VLMs to more fully exploit their underlying semantic and temporal grounding capabilities to differentiate frames based on their perceived task progress, consequently producing significantly better value predictions. Without any robot or task specific training, GVL can in-context zero-shot and few-shot predict effective values for more than 300 distinct real-world tasks across diverse robot platforms, including challenging bimanual manipulation tasks. Furthermore, we demonstrate that GVL permits flexible multi-modal in-context learning via examples from heterogeneous tasks and embodiments, such as human videos. The generality of GVL enables various downstream applications pertinent to visuomotor policy learning, including dataset filtering, success detection, and value-weighted regression -- all without any model training or finetuning.

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ICML Conference 2024 Conference Paper

Chain of Code: Reasoning with a Language Model-Augmented Code Emulator

Chengshu Li 0002
Jacky Liang
Andy Zeng 0001
Xinyun Chen
Karol Hausman
Dorsa Sadigh
Sergey Levine
Li Fei-Fei 0001

Code provides a general syntactic structure to build complex programs and perform precise computations when paired with a code interpreter – we hypothesize that language models (LMs) can leverage code-writing to improve Chain of Thought reasoning not only for logic and arithmetic tasks, but also for semantic ones (and in particular, those that are a mix of both). For example, consider prompting an LM to write code that counts the number of times it detects sarcasm in an essay: the LM may struggle to write an implementation for "detect_sarcasm(string)" that can be executed by the interpreter (handling the edge cases would be insurmountable). However, LMs may still produce a valid solution if they not only write code, but also selectively "emulate" the interpreter by generating the expected output of "detect_sarcasm(string)". In this work, we propose Chain of Code (CoC), a simple yet surprisingly effective extension that improves LM code-driven reasoning. The key idea is to encourage LMs to format semantic sub-tasks in a program as flexible pseudocode that the interpreter can explicitly catch undefined behaviors and hand off to simulate with an LM (as an "LMulator"). Experiments demonstrate that Chain of Code outperforms Chain of Thought and other baselines across a variety of benchmarks; on BIG-Bench Hard, Chain of Code achieves 84%, a gain of 12% over Chain of Thought. In a nutshell, CoC broadens the scope of reasoning questions that LMs can answer by "thinking in code".

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ICRA Conference 2024 Conference Paper

How to Prompt Your Robot: A PromptBook for Manipulation Skills with Code as Policies

Montserrat Gonzalez Arenas
Ted Xiao
Sumeet Singh
Vidhi Jain
Allen Z. Ren
Quan Vuong
Jake Varley
Alexander Herzog

Large Language Models (LLMs) have demonstrated the ability to perform semantic reasoning, planning and write code for robotics tasks. However, most methods rely on pre-existing primitives (i. e. pick, open drawer) or similar examples of robot code alone, which heavily limits their scalability to new scenarios. We present PromptBook, a collection of different prompting paradigms to generate code for successfully executing new manipulation skills. We demonstrate example-based, instruction-based and chain-of-thought to write robot code; as well as a method to build the prompt leveraging LLMs and human feedback. We show PromptBook enables LLMs to write code for new low-level manipulation skills in a zero-shot manner: from picking diverse objects, opening/closing drawers, to whisking, and waving hello. We evaluate the new skills on a mobile manipulator with 83% success rate at picking, 50-71% at opening drawers and 100% at closing them. Notably, the LLM is able to infer gripper orientation for grasping a drawer handle (z-axis aligned) vs. a top-down grasp (x-axis aligned).

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ICRA Conference 2024 Conference Paper

Open X-Embodiment: Robotic Learning Datasets and RT-X Models: Open X-Embodiment Collaboration

Abby O'Neill
Abdul Rehman
Abhiram Maddukuri
Abhishek Gupta 0004
Abhishek Padalkar
Abraham Lee
Acorn Pooley
Agrim Gupta

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. The project website is robotics-transformer-x. github.io.

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ICML Conference 2024 Conference Paper

PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs

Soroush Nasiriany
Fei Xia 0002
Wenhao Yu 0003
Ted Xiao
Jacky Liang
Ishita Dasgupta 0001
Annie Xie
Danny Driess

Vision language models (VLMs) have shown impressive capabilities across a variety of tasks, from logical reasoning to visual understanding. This opens the door to richer interaction with the world, for example robotic control. However, VLMs produce only textual outputs, while robotic control and other spatial tasks require outputting continuous coordinates, actions, or trajectories. How can we enable VLMs to handle such settings without fine-tuning on task-specific data? In this paper, we propose a novel visual prompting approach for VLMs that we call Prompting with Iterative Visual Optimization (PIVOT), which casts tasks as iterative visual question answering. In each iteration, the image is annotated with a visual representation of proposals that the VLM can refer to (e. g. , candidate robot actions, localizations, or trajectories). The VLM then selects the best ones for the task. These proposals are iteratively refined, allowing the VLM to eventually zero in on the best available answer. We investigate PIVOT on real-world robotic navigation, real-world manipulation from images, instruction following in simulation, and additional spatial inference tasks such as localization. We find, perhaps surprisingly, that our approach enables zero-shot control of robotic systems without any robot training data, navigation in a variety of environments, and other capabilities. Although current performance is far from perfect, our work highlights potentials and limitations of this new regime and shows a promising approach for Internet-Scale VLMs in robotic and spatial reasoning domains.

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ICRA Conference 2023 Conference Paper

Code as Policies: Language Model Programs for Embodied Control

Jacky Liang
Wenlong Huang
Fei Xia 0002
Peng Xu 0010
Karol Hausman
Brian Ichter
Pete Florence
Andy Zeng 0001

Large language models (LLMs) trained on code-completion have been shown to be capable of synthesizing simple Python programs from docstrings [1]. We find that these code-writing LLMs can be re-purposed to write robot policy code, given natural language commands. Specifically, policy code can express functions or feedback loops that process perception outputs (e. g. , from object detectors [2], [3]) and parameterize control primitive APIs. When provided as input several example language commands (formatted as comments) followed by corresponding policy code (via few-shot prompting), LLMs can take in new commands and autonomously re-compose API calls to generate new policy code respectively. By chaining classic logic structures and referencing third-party libraries (e. g. , NumPy, Shapely) to perform arithmetic, LLMs used in this way can write robot policies that (i) exhibit spatial-geometric reasoning, (ii) generalize to new instructions, and (iii) prescribe precise values (e. g. , velocities) to ambiguous descriptions (‘faster’) depending on context (i. e. , behavioral commonsense). This paper presents Code as Policies: a robot-centric formulation of language model generated programs (LMPs) that can represent reactive policies (e. g. , impedance controllers), as well as waypoint-based policies (vision-based pick and place, trajectory-based control), demonstrated across multiple real robot platforms. Central to our approach is prompting hierarchical code-gen (recursively defining undefined functions), which can write more complex code and also improves state-of-the-art to solve 39. 8% of problems on the HumanEval [1] benchmark. Code and videos are available at https://code-as-policies.github.io

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ICRA Conference 2022 Conference Paper

Search-Based Task Planning with Learned Skill Effect Models for Lifelong Robotic Manipulation

Jacky Liang
Mohit Sharma 0001
Alex LaGrassa
Shivam Vats
Saumya Saxena
Oliver Kroemer

Robots deployed in many real-world settings need to be able to acquire new skills and solve new tasks over time. Prior works on planning with skills often make assumptions on the structure of skills and tasks, such as subgoal skills, shared skill implementations, or task-specific plan skeletons, which limit adaptation to new skills and tasks. By contrast, we propose doing task planning by jointly searching in the space of parameterized skills using high-level skill effect models learned in simulation. We use an iterative training procedure to efficiently generate relevant data to train such models. Our approach allows flexible skill parameterizations and task specifications to facilitate lifelong learning in general-purpose domains. Experiments demonstrate the ability of our planner to integrate new skills in a lifelong manner, finding new task strategies with lower costs in both train and test tasks. We additionally show that our method can transfer to the real world without further fine-tuning.

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ICRA Conference 2021 Conference Paper

Contact Localization for Robot Arms in Motion without Torque Sensing

Jacky Liang
Oliver Kroemer

Detecting and localizing contacts is essential for robot manipulators to perform contact-rich tasks in unstructured environments. While robot skins can localize contacts on the surface of robot arms, these sensors are not yet robust or easily accessible. As such, prior works have explored using proprioceptive observations, such as joint velocities and torques, to perform contact localization. Many past approaches assume the robot is static during contact incident, a single contact is made at a time, or having access to accurate dynamics models and joint torque sensing. In this work, we relax these assumptions and propose using Domain Randomization to train a neural network to localize contacts of robot arms in motion without joint torque observations. Our method uses a novel cylindrical projection encoding of the robot arm surface, which allows the network to use convolution layers to process input features and transposed convolution layers to predict contacts. The trained network achieves a contact detection accuracy of 91. 5% and a mean contact localization error of 3. 0cm. We further demonstrate an application of the contact localization model in an obstacle mapping task, evaluated in both simulation and the real world.

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IROS Conference 2021 Conference Paper

Visual Identification of Articulated Object Parts

Vicky Zeng
Timothy E. Lee
Jacky Liang
Oliver Kroemer

As autonomous robots interact and navigate around real-world environments such as homes, it is useful to reliably identify and manipulate articulated objects, such as doors and cabinets. Many prior works in object articulation identification require manipulation of the object, either by the robot or a human. While recent works have addressed predicting articulation types from visual observations alone, they often assume prior knowledge of category-level kinematic motion models or sequence of observations where the articulated parts are moving according to their kinematic constraints. In this work, we propose FormNet, a neural network that identifies the articulation mechanisms between pairs of object parts from a single frame of an RGB-D image and segmentation masks. The network is trained on 100k synthetic images of 149 articulated objects from 6 categories. Synthetic images are rendered via a photorealistic simulator with domain randomization. Our proposed model predicts motion residual flows of object parts, and these flows are used to determine the articulation type and parameters. The network achieves an articulation type classification accuracy of 82. 5% on novel object instances in trained categories. Experiments also show how this method enables generalization to novel categories and can be applied to real-world images without fine-tuning.

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ICRA Conference 2020 Conference Paper

DexPilot: Vision-Based Teleoperation of Dexterous Robotic Hand-Arm System

Ankur Handa
Karl Van Wyk
Wei Yang 0019
Jacky Liang
Yu-Wei Chao
Qian Wan
Stan Birchfield
Nathan D. Ratliff

Teleoperation offers the possibility of imparting robotic systems with sophisticated reasoning skills, intuition, and creativity to perform tasks. However, teleoperation solutions for high degree-of-actuation (DoA), multi-fingered robots are generally cost-prohibitive, while low-cost offerings usually offer reduced degrees of control. Herein, a low-cost, depth-based teleoperation system, DexPilot, was developed that allows for complete control over the full 23 DoA robotic system by merely observing the bare human hand. DexPilot enabled operators to solve a variety of complex manipulation tasks that go beyond simple pick-and-place operations and performance was measured through speed and reliability metrics. DexPilot cost-effectively enables the production of high dimensional, multi-modality, state-action data that can be leveraged in the future to learn sensorimotor policies for challenging manipulation tasks. The videos of the experiments can be found at https://sites.google.com/view/dex-pilot.

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ICRA Conference 2020 Conference Paper

In-Hand Object Pose Tracking via Contact Feedback and GPU-Accelerated Robotic Simulation

Jacky Liang
Ankur Handa
Karl Van Wyk
Viktor Makoviychuk
Oliver Kroemer
Dieter Fox

Tracking the pose of an object while it is being held and manipulated by a robot hand is difficult for vision-based methods due to significant occlusions. Prior works have explored using contact feedback and particle filters to localize in-hand objects. However, they have mostly focused on the static grasp setting and not when the object is in motion, as doing so requires modeling of complex contact dynamics. In this work, we propose using GPU-accelerated parallel robot simulations and derivative-free, sample-based optimizers to track in-hand object poses with contact feedback during manipulation. We use physics simulation as the forward model for robot-object interactions, and the algorithm jointly optimizes for the state and the parameters of the simulations, so they better match with those of the real world. Our method runs in real-time (30Hz) on a single GPU, and it achieves an average point cloud distance error of 6mm in simulation experiments and 13mm in the real-world ones.

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IROS Conference 2019 Conference Paper

Homography-Based Deep Visual Servoing Methods for Planar Grasps

Austin S. Wang
Wuming Zhang
Daniel Troniak
Jacky Liang
Oliver Kroemer

We propose a visual servoing framework for learning to improve grasps of objects. RGB and depth images from grasp attempts are collected using an automated data collection process. The data is then used to train a Grasp Quality Network (GQN) that predicts the outcome of grasps from visual information. A grasp optimization pipeline uses homography models with the trained network to optimize the grasp success rate. We evaluate and compare several algorithms for adjusting the current gripper pose based on the current observation from a gripper-mounted camera to perform visual servoing. Evaluations in both simulated and hardware environments show considerable improvement in grasp robustness with models trained using less than 30K grasp trials. Success rates for grasping novel objects unseen during training increased from 18. 5% to 81. 0% in simulation, and from 17. 8% to 78. 0% in the real world.

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ICRA Conference 2017 Conference Paper

Design of parallel-jaw gripper tip surfaces for robust grasping

Menglong Guo
David V. Gealy
Jacky Liang
Jeffrey Mahler
Aimee Goncalves
Stephen McKinley
Juan Aparicio Ojea
Ken Goldberg

Parallel-jaw robot grippers can grasp almost any object and are ubiquitous in industry. Although the shape, texture, and compliance of gripper jaw surfaces affect grasp robustness, almost all commercially available grippers provide a pair of rectangular, planar, rigid jaw surfaces. Practitioners often modify these surfaces with a variety of ad-hoc methods such as adding rubber caps and/or wrapping with textured tape. This paper explores data-driven optimization of gripper jaw surfaces over a design space based on shape, texture, and compliance using rapid prototyping. In total, 37 jaw surface design variations were created using 3D printed casting molds and silicon rubber. The designs were evaluated with 1377 physical grasp experiments using a 4-axis robot (with automated reset). These tests evaluate grasp robustness as the probability that the jaws will acquire, lift, and hold a training set of objects at nominal grasp configurations computed by Dex-Net 1. 0. Hill-climbing in parameter space yielded a grid pattern of 0. 03 inch void depth and 0. 0375 inch void width on a silicone polymer with durometer of A30. We then evaluated performance of this design using an ABB YuMi robot grasping a set of eight difficult-to-grasp 3D printed objects in 80 grasps with four gripper surfaces. The factory-provided gripper tips succeeded in 28. 7% of the 80 trials, increasing to 68. 7% when the tips were wrapped with tape. Gripper tips with gecko-inspired surfaces succeeded in 80. 0% of trials, and gripper tips with the designed silicone surfaces succeeded in 93. 7% of trials.

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