Recent advancements in robot learning leverage large language models (LLMs) and sampling-based task and motion planning (TAMP) modules for automatic and scalable robot data generation. This method yields both success trajectories and a large number of failure trajectories. Prior works typically filter out failure data and adopt behavior cloning (BC) to train policy. However, this significantly reduces the sample efficiency of the method and results in a policy limited by the data collection behavior policy. In this paper, we introduce a vision-language-conditioned action-sequence diffusion policy and an action-sequence diffusion policy learning with Q-guided refinement for its training. We first redefine the reverse process of the diffusion model as the distribution of action sequences conditioned on visual observations and language instructions. We then employ BC to pretrain the policy on the success sub-dataset. Next, we optimize the action-sequence Q-value function by minimizing the temporal difference error across the complete dataset. Finally, we integrate guidance from the Q-value function into the BC loss of the reverse diffusion chain. Our method significantly outperforms baseline methods in terms of success rate and sample efficiency. By effectively leveraging failure data to optimize the policy, our method can achieve results comparable to those trained with the complete success sub-dataset while requiring 20%-30% less success data.

Articulated continuum robots (ACRs) are characterized by flexibility, controllability, and adaptability and perform excellently in complex and constrained environments. However, the large number of motor drives limit the ACRs' portability and make them cumbersome to control. This paper presents a novel tendon-driven ACR composed of stabilized self-locking joints (SLJs) connected in series. After triggering the mechanical constraints with shape memory alloy coils, each joint can be maintained in either a self-locking or release state with zero power consumption. Consequently, even with a single set of drive units, the ACR can operate in multiple modes, enabling variable motion performance and workspace adaptability, effectively reducing the number of motors. The ACR's stiffness also varies with the locking state of its SLJs, and no motor drive is required to maintain its shape when all SLJs are self-locking. The performance and reliability of the SLJ prototype were validated. The workspace of the ACR prototype model was analyzed, and its partial motion performance, motion error, and variable stiffness were verified.

In this work, we focus on addressing the long-horizon packing tasks in densely cluttered scenes. Such tasks require policies to effectively manage severe occlusions among objects and continually produce precise actions based on visual observations. We propose a vision-based Hierarchical policy for Cluttered-scene Long-horizon Manipulation (HCLM). It employs a high-level policy and three options to select and instantiate three parameterized action primitives: push, pick, and place. We first train the two-stream pick and place options by behavior cloning (BC). Subsequently, we use hierarchical reinforcement learning (HRL) to train the high-level policy and push option. During HRL, we propose a Spatially Extended Q-update (SEQ) to augment the updates for the push option and a Two-Stage Update Scheme (TSUS) to alleviate the non-stationary transition problem in updating the high-level policy. We demonstrate that HCLM significantly outperforms baseline methods in terms of success rate and efficiency in diverse tasks both in simulation and real world. The ablation studies also validate the key roles of SEQ and TSUS in HRL.

Vision-Language-Action (VLA) models have advanced robotic control by enabling end-to-end decision-making directly from multimodal inputs. However, their tightly coupled architectures expose novel security vulnerabilities. Unlike traditional adversarial perturbations, backdoor attacks represent a stealthier, persistent, and practically significant threat—particularly under the emerging Training-as-a-Service paradigm—but remain largely unexplored in the context of VLA models. To address this gap, we propose **BadVLA**, a backdoor attack method based on Objective-Decoupled Optimization, which for the first time exposes the backdoor vulnerabilities of VLA models. Specifically, it consists of a two-stage process: (1) explicit feature-space separation to isolate trigger representations from benign inputs, and (2) conditional control deviations that activate only in the presence of the trigger, while preserving clean-task performance. Empirical results on multiple VLA benchmarks demonstrate that BadVLA consistently achieves near-100\% attack success rates with minimal impact on clean task accuracy. Further analyses confirm its robustness against common input perturbations, task transfers, and model fine-tuning, underscoring critical security vulnerabilities in current VLA deployments. Our work offers the first systematic investigation of backdoor vulnerabilities in VLA models, highlighting an urgent need for secure and trustworthy embodied model design practices.

This paper identifies three stages in the radical technological innovation process, namely formation process in niches, breaking out of niches and entering regimes, and new regime formation. It then adopts Multi-level Perspective (MLP) to explore the formation process, operating mechanism, breakthrough path, and impact factors of radical technological innovation. A three-phase model, which includes formation of radical innovation, breakout of radical innovation, and new regimes construction, is proposed to analyze radical technological innovation. The model is adopted in a case study to analyze the leapfrogging development of technologies in China’s mobile communication industry. This paper enriches technological innovation theory and provides supports for policy making and guidance for industries/enterprises practices regarding technological innovation in emerging economies.

The paper explores the relationship among technological innovation, technological trajectory transition, and firms’ innovation performance. Technological innovation is studied from the perspectives of innovation novelty and innovation openness. Technological trajectory transition is categorized into creative cumulative technological trajectory transition and creative disruptive technological trajectory transition. A structural equation model is developed and tested with data collected by surveying 366 Chinese firms. The results indicate that both innovation novelty and innovation openness positively affects creative cumulative technological trajectory transition as well as creative disruptive technological trajectory transition. Innovation openness and creative disruptive technological trajectory transition both positively affect firms’ innovation performance. However, neither innovation novelty nor creative cumulative technological trajectory transition positively affects firms’ innovation performance. Implications for managers and directions for future studies are discussed.