VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models (2307.05973v2)

Abstract: LLMs are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation in the form of reasoning and planning. Despite the progress, most still rely on pre-defined motion primitives to carry out the physical interactions with the environment, which remains a major bottleneck. In this work, we aim to synthesize robot trajectories, i.e., a dense sequence of 6-DoF end-effector waypoints, for a large variety of manipulation tasks given an open-set of instructions and an open-set of objects. We achieve this by first observing that LLMs excel at inferring affordances and constraints given a free-form language instruction. More importantly, by leveraging their code-writing capabilities, they can interact with a vision-LLM (VLM) to compose 3D value maps to ground the knowledge into the observation space of the agent. The composed value maps are then used in a model-based planning framework to zero-shot synthesize closed-loop robot trajectories with robustness to dynamic perturbations. We further demonstrate how the proposed framework can benefit from online experiences by efficiently learning a dynamics model for scenes that involve contact-rich interactions. We present a large-scale study of the proposed method in both simulated and real-robot environments, showcasing the ability to perform a large variety of everyday manipulation tasks specified in free-form natural language. Videos and code at https://voxposer.github.io

Overview of "VoxPoser: Composable 3D Value Maps for Robotic Manipulation with LLMs"

The paper, "VoxPoser: Composable 3D Value Maps for Robotic Manipulation with LLMs," explores the synthesis of robot trajectories using the latent knowledge within LLMs in conjunction with vision-LLMs (VLMs). This work addresses a prevalent bottleneck in robotic manipulation tasks—the dependency on pre-defined motion primitives—by generating a dense sequence of 6-DoF end-effector waypoints for various manipulation tasks based on open-set instructions and objects.

Key Contributions

The research introduces a novel framework known as "VoxPoser" that leverages the reasoning capabilities of LLMs to infer affordances and constraints from language instructions, without being limited by pre-existing skill libraries. The approach involves:

• Composing 3D Value Maps: By utilizing the LLMs' ability to generate code, VoxPoser constructs dense 3D value maps that integrate both affordance and constraint information into the observation space of the agent using VLMs. These maps serve as objective functions in a model-based planning framework, facilitating zero-shot deployment of closed-loop robot trajectories.
• Use of Model-Based Planning: The proposed method employs a model-based planning framework for the synthesis of robot trajectories, ensuring robustness against dynamic perturbations and accommodating online adaptation by learning scene dynamics through limited interactions.
• Comprehensive Evaluation: The approach is validated through an extensive set of trials in both simulated and real-world robotic environments, demonstrating its capability to perform a broad array of tasks from natural language specifications.

Numerical Results and Analysis

The paper presents strong numerical results showcasing the effectiveness of VoxPoser in executing everyday manipulation tasks. The proposed method outperformed a baseline using action primitives, achieving a higher success rate and better robustness in dynamic environments. For instance, tasks such as "setting up the table" achieved success rates of 88% in static environments and 70% amidst disturbances, marking a substantial improvement over the baseline's performance.

Theoretical and Practical Implications

This research underscores a significant stride in harnessing LLMs for real-world robotic interactions, shifting from word-based reasoning to spatial compositionality within the observation space of robots. The implications are twofold:

• Practical Robotics: By reducing reliance on task-specific data collection and manually defined primitives, VoxPoser paves the way for more adaptive and generalizable robotic systems that can perform a wide range of tasks described in natural language.
• Theoretical Exploration: The paper bridges high-level language understanding with physical actions, enriching the understanding of how LLMs' internalized knowledge can be grounded in perceptual and operational spaces.

Future Directions

The promising results from VoxPoser open several avenues for future exploration:

• Enhanced Dynamics Models: Future research could explore integrating advanced learning-based dynamics models to effectively capture and predict contact-rich interactions in real-time.
• Integration with Multi-Modal LLMs: Incorporating multi-modal LLMs that process visual input directly could streamline the perceptual grounding process and enhance model robustness.
• Task-Specific Optimization: Development of enhanced trajectory optimization methods tailored to the structured output of 3D value maps could further improve manipulation efficiency.

In conclusion, "VoxPoser" represents a significant advancement in robotic manipulation, providing a framework that effectively leverages LLMs for physical task execution. This work not only demonstrates the practical utility of LLMs in robotics but also highlights the potential for further interdisciplinary developments in AI and robotics.