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ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation
Xiaoqi Li, Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, Hao Dong
TL;DR
Robot manipulation methods trained on limited simulator categories struggle with generalization and often lack interpretable low-level reasoning. ManipLLM adapts MLLMs with manipulation-specific reasoning, pose prediction, closed-loop impedance adaptation, and real-world test-time adaptation. It achieves promising performance across 30 simulator categories and strong real-world generalization with or without TTA.
Problem
Learning-based manipulation trained on limited simulator categories struggles to generalize, while low-level action prediction often lacks interpretability and common-sense reasoning.
Method
ManipLLM fine-tunes injected adapters with category identification, affordance reasoning, and pose prediction, then uses chain-of-thought inference, active impedance adaptation, and real-world TTA.
Results
ManipLLM achieves promising manipulation success across 30 simulator categories and strong real-world generalization with or without TTA.
Takeaways & Limitations
The approach supports object-centric manipulation across extensive categories while retaining common-sense reasoning and interpretable pose prediction.
Takeaways & Limitations
A sim-to-real gap remains because real-world environments and device configurations may differ significantly from simulator conditions.
Abstract
from arXiv · showhide
Robot manipulation relies on accurately predicting contact points and end-effector directions to ensure successful operation. However, learning-based robot manipulation, trained on a limited category within a simulator, often struggles to achieve generalizability, especially when confronted with extensive categories. Therefore, we introduce an innovative approach for robot manipulation that leverages the robust reasoning capabilities of Multimodal Large Language Models (MLLMs) to enhance the stability and generalization of manipulation. By fine-tuning the injected adapters, we preserve the inherent common sense and reasoning ability of the MLLMs while equipping them with the ability for manipulation. The fundamental insight lies in the introduced fine-tuning paradigm, encompassing object category understanding, affordance prior reasoning, and object-centric pose prediction to stimulate the reasoning ability of MLLM in manipulation. During inference, our approach utilizes an RGB image and text prompt to predict the end effector's pose in chain of thoughts. After the initial contact is established, an active impedance adaptation policy is introduced to plan the upcoming waypoints in a closed-loop manner. Moreover, in real world, we design a test-time adaptation (TTA) strategy for manipulation to enable the model better adapt to the current real-world scene configuration. Experiments in simulator and real-world show the promising performance of ManipLLM. More details and demonstrations can be found at https://sites.google.com/view/manipllm.
1. Introduction
ManipLLM addresses limited generalization and interpretability in object-centric robot manipulation by adapting MLLMs for category, affordance, and pose reasoning. It predicts contact points and end-effector directions through interpretable reasoning, then supports closed-loop and real-world adaptation.
- Existing manipulation methods often treat low-level action prediction as a black box, limiting interpretability and common-sense reasoning across diverse object categories.
- ManipLLM fine-tunes injected adapters while preserving MLLM reasoning, adding object category identification, affordance prior reasoning, and manipulation-aware pose prediction.The training progresses from category-level recognition to region-level affordance understanding and pose-level coordinate and direction prediction.
- The method predicts a 2D contact pixel and end-effector direction from an RGB image and text prompt, using depth to project the contact into 3D space.Its final pose includes a 3D contact point and gripper up and forward directions forming the end-effector rotation.
- After initial contact, active impedance adaptation plans subsequent waypoints in a closed loop, while test-time adaptation addresses differences between simulation and real-world scenes.The real-world strategy adapts partial model parameters using current test samples and manipulation outcomes.
- Experiments show promising simulator performance across 30 object categories and strong real-world generalization with or without test-time adaptation.
- The paper’s contributions include chain-of-thought training and inference for robust, explainable pose prediction and generalization across extensive categories.
2. Related Works
Prior robotic manipulation methods use state- or vision-based policies but often formulate manipulation as black-box prediction. ManipLLM instead injects manipulation capabilities into MLLMs while retaining their reasoning for interpretable handling of diverse objects.
- 2.1. Robotic Manipulation: State-based reinforcement learning uses pure state inputs, whereas complex scenes require vision-based observations to perceive environments and objects.
- 2.1. Robotic Manipulation: Vision-based methods predict actionable pixels, movable regions, point-wise motion flow, or 3D value maps for language-conditioned manipulation.
- 2.1. Robotic Manipulation: These approaches can reduce interpretability by casting manipulation as black-box prediction, especially across extensive object categories.
- 2.2. Multimodal Large Language Models: MLLMs provide multimodal reasoning by connecting RGB images and text, building on language models’ broad reasoning abilities.
- 2.2. Multimodal Large Language Models: Object-centric manipulation remains under-explored, motivating ManipLLM’s injection of manipulation capabilities while preserving original reasoning ability.The resulting model is described as supporting precise manipulation of diverse categories through interpretable thinking.
3. Method
ManipLLM equips a multimodal language model for object-centric manipulation through staged reasoning over object categories, affordances, and end-effector poses. It combines chain-of-thought pose inference with closed-loop impedance adaptation and test-time adaptation for real-world configurations.
- Method overview: ManipLLM fine-tunes injected adapters while preserving the MLLM’s existing perception and reasoning capabilities for manipulation.The training paradigm includes category identification, affordance prior reasoning, and manipulation-aware pose prediction.
- Object Category Identification: Object Category Identification supplies category cognition as a prior because objects in the same category share geometric properties.The model is not updated during this stage, helping retain its broad object-identification and generalization abilities.
- Affordance Prior Reasoning: Affordance Prior Reasoning converts normalized distance maps into pixel-level actionability probabilities and trains the model to distinguish positive from negative manipulation regions.Positive pixels have affordance scores above 0.8, while negative pixels have scores below the selected threshold; linguistic yes/no supervision transfers the visual prior to the language decoder.
- Pose prediction: Fine-tuning and masked language modeling train the model to predict contact coordinates and gripper directions, with continuous direction values discretized into 100 bins.Masked language modeling infills masked coordinate or direction values under cross-entropy supervision.
- Inference and control: During inference, chain-of-thought reasoning outputs the contact pixel and gripper directions, while depth projects the contact point into 3D space.After initial contact, a closed-loop impedance policy perturbs candidate directions, applies forces, and selects the direction producing the greatest observed movement.
- Sim-to-real transfer: Test-time adaptation uses real-world manipulation success or failure to refine pose-related predictions for the current scene configuration.The strategy adds an affordance-checking reasoning step and updates adapter parameters so later predictions better match the target hardware configuration.
4. Experiment Results
Experiments evaluate ManipLLM in simulation, through baselines and ablations, and in real-world manipulation with test-time adaptation. The results show broad-category generalization, module-specific gains, and adaptation to hardware-specific discrepancies.
- Experimental Setup: Approximately 10,000 successful manipulation samples across 20 categories are collected in SAPIEN using a Franka Panda with a flying suction gripper.The tasks include pulling primitives such as opening drawers and doors, rotating pliers, and lifting lids.
- Quantitative Comparison: ManipLLM is compared with Where2Act, UMPNet, Flowbot3D, and Implicit3D under the same train/test split and end-effector setting.Comparisons use the initial-movement setting because it reflects initial pose prediction and is prerequisite to long-distance movement.
- Quantitative Comparison: 69.0% success on Opening Drawer versus 14.0% for VoxPoser demonstrates the reported advantage in that task.The authors attribute VoxPoser’s difficulty to finding the correct grasping pose and suitable moving trajectory in challenging cases.
- Ablation and Analysis: Removing Chain-Of-Thought reasoning decreases performance by −3% during inference.The reported comparison supports generating the final pose through a transparent reasoning process rather than directly.
- Ablation and Analysis: Without Active Impedance Adaptation, long-distance performance decreases from 0.57 to 0.50.The policy uses force feedback under impedance control to adjust direction and produce a smoother long-term trajectory.
- Real-world Evaluation: Real-world TTA adapts predictions to hardware discrepancies, including a short suction-gripper head and collision risk near protruding handles.The strategy learns from successful and unsuccessful scenarios and is applied to current scene configurations.
5. Conclusion
ManipLLM transforms MLLMs into object-centric robotic manipulation through chain-of-thought training and pose prediction. It combines force-feedback direction adjustment with strong generalization across extensive categories and real-world settings.
- ManipLLM transforms MLLMs into robotic manipulation through chain-of-thought training and equips them to predict poses.
- Active impedance adaptation adjusts direction using force feedback to ensure a smooth moving trajectory.
- ManipLLM shows strong generalization across extensive object categories and in real-world experiments.