Articulated tracked robots face significant challenges in maintaining stable locomotion over uneven terrain due to unknown contact points between tracks and ground, which are critical for dynamic control. Unlike legged robots, where contact locations can be predicted, tracked systems require real-time adaptation to varying terrains. This paper presents C-TRAC, a terrain-adaptive control framework that integrates reinforcement learning with a contact-modeling variational autoencoder (C-VAE) to enable robust obstacle traversal. We first train a C-VAE in simulation to reconstruct high-fidelity contact information (position and binary probability) from noisy sensor measurements. This model learns a latent representation of terrain contacts, capturing complex interactions between the robot’s kinematics and environment. Subsequently, we employ an asymmetric Soft Actor-Critic (SAC) algorithm to optimize a control policy that leverages the predicted contact data for adaptive track control during locomotion. Extensive experiments validate C-TRAC in both simulated and real-world scenarios. In benchmark tests against state-of-the-art (SOTA) methods using RoboCup Rescue Robot League environments, our approach achieves superior obstacle traversal speed (up to 66.67% faster on 45◦staircase) and stability (up to 47.53% more stable on the oblique terrace) compared to contact-agnostic RL baselines and model-based methods. Notably, zero-shot sim-to-real transfer demonstrates consistent performance in unstructured outdoor ruins, also confirming the framework’s practicality.

The ability of autonomous subtask generation is important for robots to effectively cope with unforeseen situations during indoor search and rescue missions. While prior work mainly focused on improving individual low-level skills of the rescue robot, this paper proposes AutoExpand: a high-level framework that takes advantage of the extensive knowledge and reasoning abilities inherent in large language models (LLM) to understand human instructions and environmental situation. Through tight coupling LLM with behavior tree, our method enables the robot to autonomously generate reactive context-aware operational subtasks on-site without human intervention or additional training. A series of real-world experiments demonstrate that AutoExpand can effectively generate appropriate tasks for search and rescue missions, leading to a search scope increased by 34.45% when compared with traditional methods. The sample code is available at https://github.com/nubot-nudt/AutoExpand.

The evolution from motion capture and teleoperation to robot skill learning has emerged as a hotspot and critical pathway for advancing embodied intelligence. However, existing systems still face a persistent gap in simultaneously achieving four objectives: accurate tracking of full upper limb movements over extended durations (Accuracy), ergonomic adaptation to human biomechanics (Comfort), versatile data collection (e.g., force data) and compatibility with humanoid robots (Versatility), and lightweight design for outdoor daily use (Convenience). We present a wearable exoskeleton system, incorporating user-friendly immersive teleoperation and multi-modal sensing collection to bridge this gap. Due to the features of a novel shoulder mechanism with synchronized linkage and timing belt transmission, this system can adapt well to compound shoulder movements and replicate 100% coverage of natural upper limb motion ranges. Weighing 5.2 kg, NuExo supports backpack-type use and can be conveniently applied in daily outdoor scenarios. Furthermore, we develop a unified intuitive teleoperation framework and a comprehensive data collection system integrating multi-modal sensing for various humanoid robots. Experiments across distinct humanoid platforms and different users validate our exoskeleton’s superiority in motion range and flexibility, while confirming its stability in data collection and teleoperation accuracy in dynamic scenarios. The videos are available on our project website at https://nubot-nuexo.github.io/

System-on-chip (SOC) design methodology is becoming the trend in the IC industry. Integrating reusable cores from multiple sources is essential in SOC design, and different design-for-testability methodologies are usually required for testing different cores. Another issue is test integration. The purpose of this paper is to present a hierarchical test scheme for SOC with heterogeneous core test anti lest access methods. A hierarchical test manager (HTM) is proposed to generate the control signals for these cores, taking into account the IEEE P1500 Standard proposal. A standard memory BIST interface is also presented, linking the HTM and the memory BIST circuit. It can control the BIST circuit with the serial or parallel test access mechanism. The hierarchical test control scheme has low area anti pin overhead, and high flexibility. An industrial case using this scheme has been designed, showing an area overhead of only about 0.63%.