Vision-Language Model Predictive Control for Manipulation Planning and Trajectory Generation (2504.05225v1)

Abstract: Model Predictive Control (MPC) is a widely adopted control paradigm that leverages predictive models to estimate future system states and optimize control inputs accordingly. However, while MPC excels in planning and control, it lacks the capability for environmental perception, leading to failures in complex and unstructured scenarios. To address this limitation, we introduce Vision-LLM Predictive Control (VLMPC), a robotic manipulation planning framework that integrates the perception power of vision-LLMs (VLMs) with MPC. VLMPC utilizes a conditional action sampling module that takes a goal image or language instruction as input and leverages VLM to generate candidate action sequences. These candidates are fed into a video prediction model that simulates future frames based on the actions. In addition, we propose an enhanced variant, Traj-VLMPC, which replaces video prediction with motion trajectory generation to reduce computational complexity while maintaining accuracy. Traj-VLMPC estimates motion dynamics conditioned on the candidate actions, offering a more efficient alternative for long-horizon tasks and real-time applications. Both VLMPC and Traj-VLMPC select the optimal action sequence using a VLM-based hierarchical cost function that captures both pixel-level and knowledge-level consistency between the current observation and the task input. We demonstrate that both approaches outperform existing state-of-the-art methods on public benchmarks and achieve excellent performance in various real-world robotic manipulation tasks. Code is available at https://github.com/PPjmchen/VLMPC.

• The paper introduces VLMPC, integrating a vision-language model with model predictive control to translate natural language instructions into precise robotic actions.
• The framework leverages Qwen-VL for high-level task planning and visual grounding, while MPC generates smooth, obstacle-avoiding trajectories.
• Simulation results demonstrate enhanced task success rates and trajectory smoothness compared to traditional methods separating planning and control.

This paper introduces Vision-LLM Predictive Control (VLMPC), a framework designed to enable robots to perform complex manipulation tasks based on natural language instructions and visual input. The core challenge addressed is bridging the gap between high-level semantic understanding (from language) and low-level continuous control required for precise robotic manipulation. Traditional methods often struggle with interpreting abstract language commands and translating them into executable robot actions, especially in dynamic and unstructured environments.

VLMPC integrates a Vision-LLM (VLM) with Model Predictive Control (MPC). The VLM (specifically, the Qwen-VL model is mentioned) acts as a high-level planner and scene interpreter. Given a natural language instruction (e.g., "pick up the red apple") and an image of the current scene, the VLM performs two key functions:

1. Task Planning: It decomposes the high-level instruction into a sequence of sub-tasks or keyframes.
2. Target Identification: It identifies and localizes the relevant objects in the scene using bounding boxes.

The MPC component then takes over for low-level trajectory generation and execution. It uses the VLM's output (target object location and potentially intermediate goals) along with a dynamics model to optimize a sequence of actions (e.g., joint torques or velocities) over a short time horizon. The MPC aims to minimize a cost function that typically includes reaching the target configuration provided by the VLM, avoiding obstacles, and ensuring smooth motion. The process is repeated at each control step, allowing the robot to react to changes in the environment or refine its trajectory based on new visual feedback.

A key aspect of VLMPC is the integration of visual feedback directly into the control loop. The VLM continuously processes visual input to update the state estimate and target location, making the system robust to environmental changes and execution errors. The framework also proposes a specific variant, Traj-VLMPC, which focuses on generating smooth and feasible end-effector trajectories based on the VLM's plan.

Implementation Details:

• VLM: Qwen-VL is used for its ability to process image and text inputs, perform visual grounding (object localization), and generate textual outputs (plans or intermediate steps).
• MPC: A standard MPC formulation is likely used, involving:
  • State: Robot joint angles/velocities, object positions (estimated via VLM/vision).
  • Action: Robot joint commands (torques/velocities).
  • Dynamics Model: A model predicting the next state given the current state and action. This could be a learned model or a physics-based model.
  • Cost Function: Penalizes deviation from the target pose (provided by VLM), excessive control effort, and potential collisions.
  • Optimization: Solved using numerical optimization techniques at each time step.
• System Architecture: The VLM provides high-level guidance (target coordinates, sub-goals) based on language instruction and vision. The MPC uses this guidance along with its internal model and current state estimate to compute low-level control actions. Visual feedback updates the VLM's understanding and the MPC's state estimate.

graph LR A[Language Instruction] --> VLM; B[Visual Input] --> VLM; VLM --> C{Target Pose / Sub-goals}; VLM --> D[Object Localization]; C --> MPC; D --> MPC; E[Robot State] --> MPC; MPC --> F[Optimized Actions]; F --> G[Robot Execution]; G --> H[Environment]; H --> B; subgraph High-Level Planner VLM end subgraph Low-Level Controller MPC end

Evaluation:

The effectiveness of VLMPC is demonstrated through simulation experiments using environments like Robosuite and potentially real-world robot platforms (though details focus on simulation). Tasks likely involve picking, placing, pushing, or stacking objects based on language commands. Performance is evaluated based on task success rate, execution time, and trajectory smoothness compared to baseline methods that might separate planning and control or use different grounding techniques.

Practical Applications:

VLMPC offers a promising approach for developing robots capable of understanding and executing complex instructions in household, industrial, or service robotics settings. By combining the semantic reasoning power of VLMs with the robustness and optimality of MPC, it allows for more intuitive human-robot interaction and adaptable robot behavior in real-world scenarios. The framework's reliance on visual feedback makes it suitable for environments where precise object models are unavailable or where the scene changes dynamically.

Limitations & Considerations:

• Computational Cost: Both VLMs and MPC can be computationally intensive, potentially limiting real-time performance, especially the optimization step in MPC.
• VLM Accuracy: The system's performance heavily depends on the VLM's ability to accurately interpret instructions and localize objects. Errors in VLM output can lead to incorrect or failed task execution.
• Dynamics Model: The accuracy of the dynamics model used by the MPC is crucial for effective control. Learned models might require significant data, while physics-based models might not capture all real-world complexities.
• Sim-to-Real Gap: Transferring the system from simulation to a real robot often presents challenges due to differences in sensing, actuation, and dynamics.