Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observation

Generative Adversarial Self-Imitation Learning With Large Language Model Feedback for Robot Control and Navigation

Deep reinforcement learning (DRL) has achieved great success in many simulated and real-world robotic tasks. However, the difficulty of designing efficient and dense reward functions makes applying DRL to tackle complex long-horizon and open-world tasks a great challenge. Generative adversarial imitation learning (GAIL) can directly learn policies from the expert trajectories and generalize well in large and complex environments, but relies on high-quality demonstrations and can seldom surpass the performance of the demonstration. Recent work used additional human evaluative feedback to facilitate GAIL to learn faster and surpass the demonstrations, but still requires suboptimal demonstrations. Moreover, it is costly and difficult for human expert to provide relatively high-quality demonstrations and evaluative feedback for various tasks. To address the above issues, in this article, we propose generative adversarial self-imitation learning from demonstration and large language model (LLM) feedback (GASL<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>MF), since LLMs encode rich commonsense knowledge and can perform a variety of reasoning tasks. GASL<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>MF allows a robot to learn from poor demonstrations and gradually replace them with its own good trajectories evaluated by LLM feedback. Our results in four physics-based control tasks and a mobile robot navigation task show that, even with demonstrations of poor performance or not completing the task, GASL<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>MF can learn faster with close to optimal performance, and generalize well to different environments and the real world with sim-to-real adaptation. Further analysis shows that the overall distribution of LLM feedback closely resembles that of human feedback and remains closer to that of ground-truth rewards than human feedback. Finally, our GASL<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>MF method works regardless of the LLM employed, and the LLM feedback from different LLMs remain robust across tasks and even better consistency than human feedback for robot learning in some tasks. These results shed light on the potential of robot imitation learning from even poor or failed demonstrations and broaden its application to a wide range of real-world tasks.

Persistent Safety Set Guided Offline Safe Reinforcement Learning

DPAIL: Training Diffusion Policy for Adversarial Imitation Learning without Policy Optimization

Human experts employ diverse strategies to complete a task, producing to multi-modal demonstration data. Although traditional Adversarial Imitation Learning (AIL) methods have achieved notable success, they often collapse theses multi-modal behaviors into a single strategy, failing to replicate expert behaviors. To overcome this limitation, we propose DPAIL , an adversarial IL framework that leverages diffusion models as a policy class to enhance expressiveness. Building on the Adversarial Soft Advantage Fitting (ASAF) framework, which removes the need for policy optimization steps, DPAIL trains a diffusion policy using a binary cross-entropy objective to distinguish expert trajectories from generated ones. To enable optimization of the diffusion policy, we introduce a novel, tractable lower bound on the policy’s likelihood. Through comprehensive quantitative and qualitative evaluations against various baselines, we demonstrate that our method not only captures diverse behaviors but also remains robust as the number of behavior modes increases.

Learning Reach-Avoid Task with Reinforcement Learning: Vectorized Simulation and Benchmark

This work identifies that additional research is still required to claim the successful resolution of the robotic arm reach-avoid task using DRL, and presents a comprehensive benchmark for the reachavoid task that accurately captures real-world complexities without simplifications.

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) provides a powerful framework for learning from demonstrations. However, real-world tasks often exhibit substantial natural variations (e.g., picking up mugs with varying shapes), making it impractical to collect demonstrations that fully specify a new task under every possible scenario. In practice, while demonstrations for the target task are limited, it is often easier to obtain datasets of heterogeneous but related behaviors. This motivates the problem of few-shot IRL with multi-task demonstrations (FM-IRL), where an agent must learn a new task with substantial variations from only a limited number of target-task demonstrations, together with sufficient demonstrations of related tasks and online agent experience. To do so, we must both recover the expert distribution of the new task and provide guidance when the agent deviates from it. We introduce Multitask discriminator Proximity-Guided IRL (MPG), which learns two complementary reward components: (1) a generalizable discriminator that transfers shared structure across related tasks to identify expert behavior in a new task, and (2) a proximity function that measures how far a state deviates from expert behavior and provides corrective guidance during exploration. We demonstrate the effectiveness of our method on multiple challenging navigation and manipulation tasks under significant variations (e.g., object configurations, table layouts, and initial robot poses), achieving an average success rate of 81.2%, outperforming the strongest per-task baseline by an average of 24.7 percentage points.

EndoWAM: A Grounded World-Action Model for Generalizable Endoscopic Navigation

Autonomous endoscopic navigation can reduce clinicians'operational burden, yet robust control remains challenging due to tissue deformation, transient occlusions, and rapidly changing viewpoints. Existing learning-based policies typically predict actions from current observations without explicitly modeling future dynamics, limiting their robustness and reliability in safety-critical settings. World Action Models (WAMs) offer a promising alternative by coupling predictive visual dynamics with action generation, but extending them to robotic endoscopy remains challenging due to limited training data, restricted viewpoint diversity, deformable anatomy, and high inference latency. We present EndoWAM, which is, to our knowledge, the first WAM for generalizable robotic endoscopic navigation. EndoWAM introduces future grounding, which predicts task-relevant target regions in future observations from intermediate denoising features of a video world model. Specifically, EndoWAM couples a lightweight diffusion transformer for future target-region prediction with a discrete action expert through a shared predictive representation. This design injects target-aware supervision into predictive dynamics modeling, improving robustness to visual degradation and viewpoint changes while enabling real-time control in a single denoising pass. We further introduce EndoMotion, a robotic endoscopic motion dataset spanning three anatomically distinct procedures: ureteroscopy, esophagoscopy, and endoscopic retrograde cholangiopancreatography (ERCP). EndoWAM consistently outperforms all baselines and alternative grounding strategies, while demonstrating strong zero-shot generalization to unseen viewpoints, environments, and targets. These results establish EndoWAM as a predictive, target-grounded framework for accurate, generalizable, and long-horizon navigation in visually constrained endoscopic environments.