Industrial humanoid robots are increasingly expected to execute high-precision manipulation tasks, such as assembly, welding, and material handling, under dynamic and contact-rich working conditions.

Abstract Robots have the potential to assist people in homes, hospitals, warehouses, and factories, but today's systems still struggle to learn robust manipulation skills from limited data, especially in contact-rich tasks. This paper explores data-efficient robot learning approaches for contact-rich manipulation.

A 27B model that achieves one-shot doubling of strict success rates on long-horizon office automation tasks via demonstration-conditioned policy learning.

A parameter-update-free framework that distills verified experience into transferable spatial memory for frozen VLMs, achieving consistent gains across 4 VLMs and 5 spatial reasoning benchmarks.

Abstract Cross-modal foundation models are increasingly used for robotic task understanding and planning. However, connecting multimodal observations and natural language instructions to symbolic planners and executable robot actions remains a challenge. This paper addresses long-horizon robotic manipulation tasks using LLM-driven symbolic planning.

MulPlanLM: multimodal robotic task planning with vision-language models and physical feedback

Abstract Industry 5.0 promotes collaborative manufacturing environments where humans and robots work together, leveraging their complementary strengths to improve productivity and safety. In such settings, trust is fundamental to ensuring effective collaboration between humans and robots. This paper presents a multi-modal model for trust evaluation in industrial human-robot collaboration settings.

Dual-arm mobile manipulators can transport and manipulate large objects with simple end-effectors. Interacting with dynamic environments subject to strict safety and compliance requirements, achieving whole-body motion planning online while maintaining compliance is challenging. This paper presents an efficient whole-body model predictive control approach for online compliant dual-arm mobile manipulation.

Insect locomotion exhibits remarkable adaptability and flexibility despite the limited scale of its nervous system. This paper extracts and models underlying leg coordination principles via adversarial inverse reinforcement learning, enabling transferable robot locomotion from limited biological data.

Dexterous hands and robotic manipulators as physical interfaces for diverse environments. Proposes bio-functional mimicry beyond structural biomimicry, enabling cross-scale manipulation and limb-tool integration for versatile robotic tasks.