Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation

• URL: http://arxiv.org/abs/2406.09738v1
• Date: Fri, 14 Jun 2024 05:53:00 GMT
• Title: Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation
• Authors: Teli Ma, Jiaming Zhou, Zifan Wang, Ronghe Qiu, Junwei Liang,
• Abstract summary: We present Sigma-Agent, an end-to-end imitation learning agent for multi-task robotic manipulation. Sigma-Agent incorporates contrastive Imitation Learning (contrastive IL) modules to strengthen vision-language and current-future representations. Sigma-Agent shows substantial improvement over state-of-the-art methods under diverse settings.
• Score: 14.354318744503088
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Developing robots capable of executing various manipulation tasks, guided by natural language instructions and visual observations of intricate real-world environments, remains a significant challenge in robotics. Such robot agents need to understand linguistic commands and distinguish between the requirements of different tasks. In this work, we present Sigma-Agent, an end-to-end imitation learning agent for multi-task robotic manipulation. Sigma-Agent incorporates contrastive Imitation Learning (contrastive IL) modules to strengthen vision-language and current-future representations. An effective and efficient multi-view querying Transformer (MVQ-Former) for aggregating representative semantic information is introduced. Sigma-Agent shows substantial improvement over state-of-the-art methods under diverse settings in 18 RLBench tasks, surpassing RVT by an average of 5.2% and 5.9% in 10 and 100 demonstration training, respectively. Sigma-Agent also achieves 62% success rate with a single policy in 5 real-world manipulation tasks. The code will be released upon acceptance.

Related papers

• Mechanistic Finetuning of Vision-Language-Action Models via Few-Shot Demonstrations [76.79742393097358]
  Vision-Language Action (VLAs) models promise to extend the remarkable success of vision-language models (VLMs) to robotics.<n>Existing fine-tuning methods lack specificity, adapting the same set of parameters regardless of a task's visual, linguistic, and physical characteristics.<n>Inspired by functional specificity in neuroscience, we hypothesize that it is more effective to finetune sparse model representations specific to a given task.
  arXiv  Detail & Related papers  (2025-11-27T18:50:21Z)
• Language-Conditioned Representations and Mixture-of-Experts Policy for Robust Multi-Task Robotic Manipulation [1.731102560795011]
  We propose a framework combining a Language-Conditioned Visual Representation (LCVR) module and a Language-conditioned Mixture-of-Experts Density Policy (LMoE-DP)<n>On real-robot benchmarks, LCVR boosts Action Chunking with Transformers (ACT) and Diffusion Policy (DP) success rates by 33.75% and 25%, respectively.<n>Our work shows that combining semantic grounding and expert specialization enables robust, efficient multi-task manipulation.
  arXiv  Detail & Related papers  (2025-10-28T04:27:03Z)
• RoBridge: A Hierarchical Architecture Bridging Cognition and Execution for General Robotic Manipulation [90.81956345363355]
  RoBridge is a hierarchical intelligent architecture for general robotic manipulation.<n>It consists of a high-level cognitive planner (HCP) based on a large-scale pre-trained vision-language model (VLM)<n>It unleashes the procedural skill of reinforcement learning, effectively bridging the gap between cognition and execution.
  arXiv  Detail & Related papers  (2025-05-03T06:17:18Z)
• LLARVA: Vision-Action Instruction Tuning Enhances Robot Learning [50.99807031490589]
  We introduce LLARVA, a model trained with a novel instruction tuning method to unify a range of robotic learning tasks, scenarios, and environments. We generate 8.5M image-visual trace pairs from the Open X-Embodiment dataset in order to pre-train our model. Experiments yield strong performance, demonstrating that LLARVA performs well compared to several contemporary baselines.
  arXiv  Detail & Related papers  (2024-06-17T17:55:29Z)
• Bridging Language, Vision and Action: Multimodal VAEs in Robotic Manipulation Tasks [0.0]
  In this work, we focus on unsupervised vision-language--action mapping in the area of robotic manipulation. We propose a model-invariant training alternative that improves the models' performance in a simulator by up to 55%. Our work thus also sheds light on the potential benefits and limitations of using the current multimodal VAEs for unsupervised learning of robotic motion trajectories.
  arXiv  Detail & Related papers  (2024-04-02T13:25:16Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning [49.92517970237088]
  We tackle the problem of training a robot to understand multimodal prompts. This type of task poses a major challenge to robots' capability to understand the interconnection and complementarity between vision and language signals. We introduce an effective framework that learns a policy to perform robot manipulation with multimodal prompts.
  arXiv  Detail & Related papers  (2023-10-14T22:24:58Z)
• RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking [54.776890150458385]
  We develop an efficient system for training universal agents capable of multi-task manipulation skills. We are able to train a single agent capable of 12 unique skills, and demonstrate its generalization over 38 tasks. On average, RoboAgent outperforms prior methods by over 40% in unseen situations.
  arXiv  Detail & Related papers  (2023-09-05T03:14:39Z)
• VIMA: General Robot Manipulation with Multimodal Prompts [82.01214865117637]
  We show that a wide spectrum of robot manipulation tasks can be expressed with multimodal prompts. We develop a new simulation benchmark that consists of thousands of procedurally-generated tabletop tasks. We design a transformer-based robot agent, VIMA, that processes these prompts and outputs motor actions autoregressively.
  arXiv  Detail & Related papers  (2022-10-06T17:50:11Z)
• BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning [108.41464483878683]
  We study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks. We develop an interactive and flexible imitation learning system that can learn from both demonstrations and interventions. When scaling data collection on a real robot to more than 100 distinct tasks, we find that this system can perform 24 unseen manipulation tasks with an average success rate of 44%.
  arXiv  Detail & Related papers  (2022-02-04T07:30:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.