Dr-BA delivers a separable optimization approach for direct radar bundle adjustment and cross-session localization using full spinning-radar intensity images, achieving state-of-the-art performance on over 200 km of on-road data.
Yoon, Richard Poulton, John Marshall, and Timothy D
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RoboTALES uses hierarchical LLM subgoals and VLM reward feedback to keep video-model futures task-aligned, then trains robot policies that beat baselines on RoboCasa and LIBERO10 long-horizon tasks.
Robo-Blocks is an LLM-augmented block-based tool that supplies generative scaffolding via structured narratives; a deployment study with novices surfaced user personas, usage patterns, and design insights for integrating such scaffolding into social-robot programming practice.
citing papers explorer
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Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization
Dr-BA delivers a separable optimization approach for direct radar bundle adjustment and cross-session localization using full spinning-radar intensity images, achieving state-of-the-art performance on over 200 km of on-road data.
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RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures
RoboTALES uses hierarchical LLM subgoals and VLM reward feedback to keep video-model futures task-aligned, then trains robot policies that beat baselines on RoboCasa and LIBERO10 long-horizon tasks.
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Robo-Blocks: Generative Scaffolding in End-User Design and Programming of Social Robots
Robo-Blocks is an LLM-augmented block-based tool that supplies generative scaffolding via structured narratives; a deployment study with novices surfaced user personas, usage patterns, and design insights for integrating such scaffolding into social-robot programming practice.