Developing robotic agents that can generalize across diverse environments while continually evolving their behaviors is a core challenge in AI and robotics. The difficulties lie in solving increasingly complex tasks and ensuring agents can continue learning without converging on narrow, specialized solutions. Quality Diversity (QD) [1, 2] methods effectively foster diversity but often rely on trial and error, where the path to a final solution can be convoluted, leading to inefficiencies and uncertainty. Our approach draws inspiration from nature's inheritance process, where offspring not only receive but also build upon the knowledge of their predecessors. Similarly, our agents inherit distilled behaviors from previous generations, allowing them to adapt and continue learning efficiently, eventually surpassing their predecessors. This natural knowledge transfer reduces randomness, guiding exploration toward more meaningful learning without manual intervention like reward shaping or task descriptors. What sets our method apart is that it offers a straightforward, evolution-inspired way to consolidate and progress, avoiding the need for manually defined styles or gradient editing [3, 4] to prevent forgetting. The agent's ability to retain and refine skills is driven by a blend of IL and RL, naturally passing down essential behaviors while implicitly discarding inferior ones. We introduce Parental Guidance (PG-1) which makes the following contributions: 1. Distributed Evolution Framework: We propose a framework that distributes the evolution process across multiple compute instances, efficiently scheduling and analyzing evolution.

Simulation has been pivotal in recent robotics milestones and is poised to play a prominent role in the field's future. However, recent robotic advances often rely on expensive and high-maintenance platforms, limiting access to broader robotics audiences. This work introduces Wheeled Lab, a framework for the low-cost, open-source wheeled platforms that are already widely established in education and research. Through integration with Isaac Lab, Wheeled Lab introduces modern techniques in Sim2Real, such as domain randomization, sensor simulation, and end-to-end learning, to new user communities. To kickstart education and demonstrate the framework's capabilities, we develop three state-of-the-art policies for small-scale RC cars: controlled drifting, elevation traversal, and visual navigation, each trained in simulation and deployed in the real world. By bridging the gap between advanced Sim2Real methods and affordable, available robotics, Wheeled Lab aims to democratize access to cutting-edge tools, fostering innovation and education in a broader robotics context. The full stack, from hardware to software, is low cost and open-source.

The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. However, creating such datasets is challenging: collecting robot manipulation data in diverse environments poses logistical and safety challenges and requires substantial investments in hardware and human labour. As a result, even the most general robot manipulation policies today are mostly trained on data collected in a small number of environments with limited scene and task diversity. In this work, we introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset with 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 84 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months. We demonstrate that training with DROID leads to policies with higher performance and improved generalization ability. We open source the full dataset, policy learning code, and a detailed guide for reproducing our robot hardware setup.

MuSHR is a low-cost, open-source robotic racecar platform for education and research, developed by the Personal Robotics Lab in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. MuSHR aspires to contribute towards democratizing the field of robotics as a low-cost platform that can be built and deployed by following detailed, open documentation and do-it-yourself tutorials. A set of demos and lab assignments developed for the Mobile Robots course at the University of Washington provide guided, hands-on experience with the platform, and milestones for further development. MuSHR is a valuable asset for academic research labs, robotics instructors, and robotics enthusiasts.

We use static object data to improve success detection for stacking objects on and nesting objects in one another. Such actions are necessary for certain robotics tasks, e.g., clearing a dining table or packing a warehouse bin. However, using an RGB-D camera to detect success can be insufficient: same-colored objects can be difficult to differentiate, and reflective silverware cause noisy depth camera perception. We show that adding static data about the objects themselves improves the performance of an end-to-end pipeline for classifying action outcomes. Images of the objects, and language expressions describing them, encode prior geometry, shape, and size information that refine classification accuracy. We collect over 13 hours of egocentric manipulation data for training a model to reason about whether a robot successfully placed unseen objects in or on one another. The model achieves up to a 57% absolute gain over the task baseline on pairs of previously unseen objects.