Failing to Grasp the Point: Hierarchical Reinforcement Learning for Grasping Tasks

Exploring High-Level Goal Prediction for Hierarchical Imitation Learning in Robotic Manipulation

WARL: Wrench-Augmented Reinforcement Learning for Task-Agnostic Learning in Legged Robots

This study proposes a new method, Wrench-Augmented Reinforcement Learning (WARL), which introduces a wrenche (force and torque) into the action space, and shows that introducing a wrench can encourage behaviors that do not sufficiently exploit the robot's physical embodiment.

Lightweight Adaptation of Pretrained Robot Manipulation Systems: Two Approaches

Two systematic attempts to improve large pretrained models with minimal or zero modification to their weights via reinforcement learning on a frozen OpenVLA-7B using binary task-success rewards on LIBERO-Goal reveal a common ceiling.

RL Bootstrapping of OpenVLA-OFT for a Novel Robot Embodiment

Adapting a pretrained vision-language-action (VLA) policy to a new robot usually assumes embodiment-specific demonstrations. This assumption is especially restrictive for custom robots whose morphology differs strongly from the manipulators seen in large robot datasets. We study a harder setting: zero-demo embodiment alignment of OpenVLA-OFT on a cable-driven parallel robot (CDPR) with a simple gripper and a previously unseen control interface. Instead of supervised fine-tuning, we use reinforcement learning in simulation with dense geometric rewards computed from simulator state. The training is performed in two stages: a PPO stage for directional motion primitives, followed by GRPO continuation from the PPO checkpoint with an expanded instruction space that includes object-conditioned commands. On the four shared directional instructions, the average held-out success rate improves from 34.25\% after PPO to 53.50\% after PPO$\rightarrow$GRPO, with especially large gains on \texttt{move left} and \texttt{move backward}. In the GRPO stage we additionally introduce \texttt{move to} over eight target objects and obtain 39/400 = 9.75\% strict success, while qualitative rollouts frequently show correct target-directed approach behavior before late-stage instability. Compared with prior OpenVLA and OpenVLA-OFT results, which rely on demonstration datasets and mostly standard rigid-arm embodiments, our method uses no embodiment-specific dataset at all. The results do not yet establish robust manipulation, but they provide stronger evidence that RL-only bootstrapping can create the first usable language-conditioned controller for a genuinely novel embodiment.

Efficient Real-World Online Reinforcement Learning for Robot Manipulation via Centralized Training and Critic Decomposition

A unified framework that combines centralized training with decentralized execution (CTDE) and a Hybrid Reward Architecture (HRA) is introduced that enables multiple actors to share a centralized multi-head critic and substantially improves both sample efficiency and policy performance.

Prompted to Explore: Training-Time-Only LLM Proposals for Sample-Efficient Pushing Grasping Policies

Learning long-horizon robot manipulation remains difficult and time-consuming, especially under sparse rewards due to inefficient exploration and reward assignment. We present a minimal integration of large language models (LLMs) with reinforcement learning (RL) in which the LLM is used strictly as an online action proposer during early training to help with the RL agent. Given a task description and a compact scene abstraction, the LLM outputs a single parameterised primitive (pushing or grasping) that is validated by lightweight safety checks and mixed with policy actions via an epsilon-greedy scheduler. Besides, we study two simple policies that preserve the same low-level learner (grasping-biased model): (i) pushing-first reward shaping that temporarily increases the relative reward of pushing early in an episode, and (ii) a Proximal Policy Optimization (PPO) high-level switch that explicitly selects between push and grasp while Soft Actor-Critic (SAC) handles low-level parameterisation. In simulation, LLM-guided exploration accelerates early learning, and the proposed methods help further mitigate grasping overuse in the cluttered environment. Besides, the LLM-guided RL agent could achieve a higher success rate at 89.5% compared with the baseline method at 68%.