Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models

Contact-rich tasks pose significant challenges for robotic systems due to inherent uncertainty, complex dynamics, and the high risk of damage during interaction. Recent advances in learning-based control have shown great potential in enabling robots to acquire and generalize complex manipulation skills in such environments, but ensuring safety, both during exploration and execution, remains a critical bottleneck for reliable real-world deployment. This survey provides a comprehensive overview of safe learning-based methods for robot contact-rich tasks. We categorize existing approaches into two main domains: safe exploration and safe execution. We review key techniques, including constrained reinforcement learning, risk-sensitive optimization, uncertainty-aware modeling, control barrier functions, and model predictive safety shields, and highlight how these methods incorporate prior knowledge, task structure, and online adaptation to balance safety and efficiency. A particular emphasis of this survey is on how these safe learning principles extend to and interact with emerging robotic foundation models, especially vision-language models (VLMs) and vision-language-action models (VLAs), which unify perception, language, and control for contact-rich manipulation. We discuss both the new safety opportunities enabled by VLM/VLA-based methods, such as language-level specification of constraints and multimodal grounding of safety signals, and the amplified risks and evaluation challenges they introduce. Finally, we outline current limitations and promising future directions toward deploying reliable, safety-aligned, and foundation-model-enabled robots in complex contact-rich environments. More details and materials are available at our \href{ https://github.com/jack-sherman01/Awesome-Learning4Safe-Contact-rich-tasks}{Project GitHub Repository}.

ManiFeel: Benchmarking and Understanding Visuotactile Manipulation Policy Learning

Supervised visuomotor policies have shown strong performance in robotic manipulation but often struggle in tasks with limited visual input, such as operations in confined spaces, dimly lit environments, or scenarios where perceiving the object's properties and state is critical for task success. In such cases, tactile feedback becomes essential for manipulation. While the rapid progress of supervised visuomotor policies has benefited greatly from high-quality, reproducible simulation benchmarks in visual imitation, the visuotactile domain still lacks a similarly comprehensive and reliable benchmark for large-scale and rigorous evaluation. To address this, we introduce ManiFeel, a reproducible and scalable simulation benchmark for studying supervised visuotactile manipulation policies across a diverse set of tasks and scenarios. ManiFeel presents a comprehensive benchmark suite spanning a diverse set of manipulation tasks, evaluating various policies, input modalities, and tactile representation methods. Through extensive experiments, our analysis reveals key factors that influence supervised visuotactile policy learning, identifies the types of tasks where tactile sensing is most beneficial, and highlights promising directions for future research in visuotactile policy learning. ManiFeel aims to establish a reproducible benchmark for supervised visuotactile policy learning, supporting progress in visuotactile manipulation and perception. To facilitate future research and ensure reproducibility, we will release our codebase, datasets, training logs, and pretrained checkpoints. Please visit the project website for more details: https://zhengtongxu.github.io/manifeel-website/

In-Hand Singulation and Scooping Manipulation with a 5 DOF Tactile Gripper

Manipulation tasks often require a high degree of dexterity, typically necessitating grippers with multiple degrees of freedom (DoF). While a robotic hand equipped with multiple fingers can execute precise and intricate manipulation tasks, the inherent redundancy stemming from its extensive DoF often adds unnecessary complexity. In this paper, we introduce the design of a tactile sensor-equipped gripper with two fingers and five DoF. We present a novel design integrating a GelSight tactile sensor, enhancing sensing capabilities and enabling finer control during specific manipulation tasks. To evaluate the gripper's performance, we conduct experiments involving two challenging tasks: 1) retrieving, singularizing, and classification of various objects embedded in granular media, and 2) executing scooping manipulations of credit cards in confined environments to achieve precise insertion. Our results demonstrate the efficiency of the proposed approach, with a high success rate for singulation and classification tasks, particularly for spherical objects at high as 94.3%, and a 100% success rate for scooping and inserting credit cards.