CrashAgent: Crash Scenario Generation via Multi-modal Reasoning

Training and evaluating autonomous driving algorithms requires a diverse range of scenarios. However, most available datasets predominantly consist of normal driving behaviors demonstrated by human drivers, resulting in a limited number of safety-critical cases. This imbalance, often referred to as a long-tail distribution, restricts the ability of driving algorithms to learn from crucial scenarios involving risk or failure, scenarios that are essential for humans to develop driving skills efficiently. To generate such scenarios, we utilize Multi-modal Large Language Models to convert crash reports of accidents into a structured scenario format, which can be directly executed within simulations. Specifically, we introduce CrashAgent, a multi-agent framework designed to interpret multi-modal real-world traffic crash reports for the generation of both road layouts and the behaviors of the ego vehicle and surrounding traffic participants. We comprehensively evaluate the generated crash scenarios from multiple perspectives, including the accuracy of layout reconstruction, collision rate, and diversity. The resulting high-quality and large-scale crash dataset will be publicly available to support the development of safe driving algorithms in handling safety-critical situations.

Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications

Sim-to-real transfer remains a significant challenge in robotics due to the discrepancies between simulated and real-world dynamics. Traditional methods like Domain Randomization often fail to capture fine-grained dynamics, limiting their effectiveness for precise control tasks. In this work, we propose a novel approach that dynamically adjusts simulation environment parameters online using in-context learning. By leveraging past interaction histories as context, our method adapts the simulation environment dynamics to real-world dynamics without requiring gradient updates, resulting in faster and more accurate alignment between simulated and real-world performance. We validate our approach across two tasks: object scooping and table air hockey. In the sim-to-sim evaluations, our method significantly outperforms the baselines on environment parameter estimation by 80% and 42% in the object scooping and table air hockey setups, respectively. Furthermore, our method achieves at least 70% success rate in sim-to-real transfer on object scooping across three different objects. By incorporating historical interaction data, our approach delivers efficient and smooth system identification, advancing the deployment of robots in dynamic real-world scenarios. Demos are available on our project page: https://sim2real-capture.github.io/

Mechanical Evidence for the Phylogenetic Origin of the Red Panda's False Thumb as an Adaptation to Arboreal Locomotion

We constructed a modular, biomimetic red panda paw with which to experimentally investigate the evolutionary reason for the existence of the false thumbs of red pandas. These thumbs were once believed to have shared a common origin with the similar false thumbs of giant pandas; however, the discovery of a carnivorous fossil ancestor of the red panda that had false thumbs implies that the red panda did not evolve its thumbs to assist in eating bamboo, as the giant panda did, but rather evolved its thumbs for some other purpose. The leading proposal for this purpose is that the thumbs developed to aid arboreal locomotion. To test this hypothesis, we conducted grasp tests on rods 5-15 mm in diameter using a biomimetic paw with 0-16 mm interchangeable thumb lengths. The results of these tests demonstrated an optimal thumb length of 7 mm, which is just above that of the red panda's true thumb length of 5.5 mm. Given trends in the data that suggest that smaller thumbs are better suited to grasping larger diameter rods, we conclude that the red panda's thumb being sized below the optimum length suggests an adaptation toward grasping branches as opposed to relatively thinner food items, supporting the new proposal that the red panda's thumbs are an adaptation primary to climbing rather than food manipulation.