Sample-Efficient Robot Skill Learning for Construction Tasks: Benchmarking Hierarchical Reinforcement Learning and Vision-Language-Action Model

Training Robot Policies with a Foundation Model Teacher

Simple-to-Complex Structured Demonstrations for Vision-Language-Action Learning

Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation by integrating visual perception, language understanding, and robot action generation. Existing research has primarily focused on improving model architectures, training strategies, and dataset scale, while little attention has been paid to how demonstrations are collected and organized. We identify demonstration organization as a fundamental yet overlooked aspect of imitation learning, as it directly affects policy learning efficiency, training stability, and policy generalization. To address this gap, we propose a simple-to-complex structured demonstration collection strategy for VLA learning using a dual-arm robotic platform. Our approach systematically organizes data through three general principles: (i) decomposing complex manipulation tasks into progressively learnable sub-skills, (ii) standardizing the interaction environment to reduce unnecessary variability, and (iii) organizing demonstrations according to progressively increasing task complexity. This structured design enables VLA models to first acquire fundamental manipulation skills before learning increasingly complex task compositions, facilitating more effective learning of long-horizon manipulation tasks. We evaluate the proposed strategy on two representative robotic manipulation tasks: block grasping and sorting, and towel folding. Experimental results show consistent improvements in task success rate and training stability compared with the baseline method of directly collecting end-to-end complete task trajectories. These findings highlight demonstration organization as a previously underexplored but important factor in VLA learning and provide practical insights into efficient skill acquisition, scalable dataset construction, and long-horizon robotic manipulation.

Hybrid Large Language Model-Reinforcement Learning Pipeline to Enhance Simulation Training for Robotics

Despite rapid advances in artificial intelligence, robotic systems remain limited by poor generalisation across unstructured environments and fragile training pipelines. Reinforcement learning (RL) has shown promise in training robotics, yet its effectiveness is often constrained by manually engineered reward mechanisms. In parallel, large language models (LLMs) demonstrate strong reasoning and evaluation capabilities that remain underutilised in robotic training pipelines. This paper proposes a hybrid LLM-RL framework in which an LLM dynamically evaluates robot performance during simulation training and adaptively modifies the reward weights to improve learning stability, accuracy of task completion, and policy convergence. Unlike existing work that focuses on natural language control at inference time, the proposed method leverages the LLM during training, acting as a high-level reward critic. We implemented this framework using an open-source robotic arm trained in simulation to demonstrate improved task success rates and learning efficiency compared to static reward mechanisms. This work highlights a scalable pathway toward more adaptive and generalisable robotic training systems for advanced robotics.

Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision Tasks

Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents. However, LLM-based agents struggle with long-horizon sequential decision tasks that require precise action optimization and environment interaction. Reinforcement Learning (RL), while effective for sequential control, often lacks the high-level abstraction and task decomposition abilities needed for complex scenarios. This paper introduces an LLM-Augmented Reinforcement Learning Agent that integrates LLM-driven planning with RL-based action optimization. The proposed architecture leverages the LLM to generate subgoals, structured plans, and contextual guidance, while the RL agent refines low-level actions through interaction with the environment. Experiments on sequential decision tasks demonstrate improved sample efficiency, higher success rates, and more coherent action trajectories compared to RL-only and LLM-only baselines. This hybrid paradigm highlights a promising direction for building more capable autonomous systems.

How To Build End-To-End Physical AI Systems for Robots

Building intelligent robots that map sensor inputs and language instructions directly to actions, is a rapidly advancing area, but progress is bottlenecked by the cost of real-world data and the risk of physical testing. This short course presents a unified pipeline for developing humanoid and general-purpose robot policies, spanning synthetic data generation, policy training, and deployment. At the core of the pipeline, world foundation models serve a dual role: generating diverse training distributions from limited teleoperation data through controllable augmentation, and acting directly as action policies by predicting future states conditioned on language and sensory context. These capabilities feed into reinforcement and imitation learning in high-fidelity simulation, where cross-embodiment vision-language-action (VLA) models learn robust sensorimotor policies. The resulting policies are then deployed on humanoid platforms via real-time edge inference, closing the loop from data generation to physical execution. Participants will gain a practical understanding of how simulation, world models, and foundation models compose into a scalable, end-to-end system for generalizable physical AI.

Exp2VLA: Enabling Vision–Language–Action for Drone Navigation from Expert Demonstrations

Vision-language-action (VLA) models open a new path toward intuitive robot control by directly linking perception, language, and action in a single end-to-end framework. Yet for UAVs, practical adoption remains difficult because existing solutions are either computationally heavy or insufficiently capable in complex environments. In this work, we propose a practical expert-distillation pipeline (Exp2VLA) for language-conditioned drone navigation. The core idea is to distill expert behavior, obtained from reinforcement learning, teleoperation, or other controllers, into training data that can be used to fine-tune compact VLA models. This allows existing control strategies to be transferred into a unified language-guided navigation model, reducing manual system integration and lowering the barrier for deploying new robot behaviors. Experiments in both sim-to-sim and simulation-in-the-loop settings across multi-object scenes show that the fine-tuned models can handle varied semantic commands and generalize to unseen target compositions. The proposed framework demonstrates how expert-policy distillation can help mechatronic systems move from specialized control modules toward more flexible and reusable robot intelligence.