Joo et al. present findings on the use of the 310LR-C Dual-Mode Lever in soft electrostatic actuators, resulting in a significant performance improvement for jumping robots. This research could inform future developments in robotics and actuator design.
Joo et al. (Nature Communications) used our 310LR-C Dual-Mode Lever in key experiments on ultralight soft electrostatic actuators. The result? A jumping robot achieving 60% greater jump height:
The LeWorldModel demonstrates effective ball localization and next-frame prediction after extensive training, showcasing advancements in action conditioning. This could inform future model development for real-time game AI applications.
After 100 epochs of JEPA training on ≈6,000 frames, the LeWorldModel has learned to:
Localise the ball with measurable structure
Predict next-frame ball position through the ARPredictor
Localise the paddle with action context confirming working action conditioning
This tweet discusses a novel soft robot design that utilizes heat-responsive materials and embedded electronics for movement without traditional mechanical systems. Senior engineers may find the innovative approach to robotics and materials science relevant for future applications in AI and automation.
A new origami-inspired soft robot uses heat-responsive materials and embedded electronics to move, fold, and reshape itself, without motors, pumps, or bulky mechanical systems.
@Princeton
ConvApparel is a new dataset aimed at improving LLM-based user simulators by quantifying the 'realism gap.' This could be relevant for engineers focused on enhancing conversational agent training methodologies.
Introducing ConvApparel, a new human-AI conversation dataset, as well as a comprehensive evaluation framework designed to quantify the "realism gap" in LLM-based user simulators and improve the training of robust conversational agents.
Read all about it →
goo.gle/41k5eff