Recent advances in vision-language-action (VLA) models have demonstrated impressive generalization for robotic manipulation. However, these models often operate by directly mapping visual and linguistic inputs to subsequent actions, lacking intermediate task planning, along with failure detection and recovery ability. These limitations prevent them from effectively decomposing complex tasks, recognizing problems, and correcting erroneous actions, ultimately resulting in complete task failure. This significantly hinders their ability to perform long-horizon tasks and generalization ability. To this end, we introduce TCoT: Trajectory Chain-of-Thought, a unified VLA framework that enhances this direct mapping with trajectory planning as well as failure detection and recovery. TCoT leverages hierarchy trajectories as a precise and compact representation of CoT reasoning for manipulation: global planning provides a high-level, goal-oriented trajectory to guide the robot toward its task objective, while local planning focuses on real-time adjustments to address dynamic changes. Moreover, we designed the Global-Local Switching Recovery algorithm that detects and effectively recovers from failures. Experimental results reveal that TCoT surpasses the state-of-the-art methods across both real and simulated scenarios and exhibits superior generalization capabilities.

Books, as enduring repositories of cultural heritage as well as knowledge, play a fundamental role in human development. Although advances in embodied AI and robotics revolutionize automation in domains, e.g., manufacturing and logistics, robotic book manipulation remains an underexplored frontier. Two primary bottlenecks impede progress: (1) scarcity of fine-grained annotated datasets for benchmarking robotic book manipulation, and (2) lack of unified perception-action frameworks capable of dynamically coupling multi-modal sensing and manipulation in real-world scenarios. To these issues, we present THU-Book, the first open-access benchmark featuring 643 3D scene captures, encompassing 11,298 high-fidelity book instances with rich annotations to support tasks from book recognition and localization to grasping and repositioning. Building upon this foundation, we develop BookBot, a novel voice-interactive book manipulation pipeline to support cross-environmental, multilingual, and multi-categorical book manipulation. First, we utilize Large Language Models (LLMs) to parse and comprehend ambiguity in user instructions. We further propose an instance segmentation module combined with OCR tool to link language to visual instances. Finally, we introduce a PCA-based manipulation policy to refine the robotic grasp pose, utilizing the principal components of the books’ geometry, improving the precision and efficiency of grasping. Experiments conducted on the THU-Book benchmark validate the effectiveness of our BookBot. The dataset is available at https://github.com/wanghq-public/BookBot.

While research has shown that the agile chip design methodology is promising to sustain the scaling of computing performance in a more efficient way, it is still of limited usage in actual applications due to two major obstacles: 1) Lack of tool-chain and developing framework supporting agile chip design, especially for large-scale modern processors. 2) The conventional verification methods are less agile and become a major bottleneck of the entire process. To tackle both issues, we propose MINJIE, an open-source platform supporting agile processor development flow. MINJIE integrates a broad set of tools for logic design, functional verification, performance modelling, pre-silicon validation and debugging for better development efficiency of state-of-the-art processor designs. We demonstrate the usage and effectiveness of MINJIE by building two generations of an open-source superscalar out-of-order RISC-V processor code-named XIANGSHAN using agile methodologies. We quantify the performance of XIANGSHAN using SPEC CPU2006 benchmarks and demonstrate that XIANGSHAN achieves industry-competitive performance.