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Jason Liu

@JasonJZLiu • 2,692 subscribers

PhD student @CMU_Robotics | Prev @NvidiaAI @UofT

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Excited to finally share Pixel: a full-stack robot learning project Tony Tao @ CoRL and I worked on earlier this year! We’re releasing two papers: 1. Pixel: A low-cost mobile manipulator, co-designed with its UMI system and learning stack. 2. Pixel-UMI: Improved UMI pipeline for learning environment-aware, whole-body manipulation (1/N)

Excited to finally share Pixel: a full-stack robot learning project Tony Tao @ CoRL and I worked on earlier this year! We’re releasing two papers: 1. Pixel: A low-cost mobile manipulator, co-designed with its UMI system and learning stack. 2. Pixel-UMI: Improved UMI pipeline for learning environment-aware, whole-body manipulation (1/N)

70,229 просмотров • 9 дней назад

💥Introducing FACTR 2, learning external force sensing on commodity robot arms without needing dedicated sensors. We show that learned force signals enable force-feedback teleop on low-cost arms and improve BC policies. FACTR 2 consists of: 1. Neural External Torque (NEXT): learns external forces without needing dedicated force sensors. 2. Force-Informed Re-Sampling Training (FIRST): uses the learned force signal to identify task-critical regions and upsample them during training. w/ Steven Oh @ CoRL Tony Tao @ CoRL 🧵(1/N)

💥Introducing FACTR 2, learning external force sensing on commodity robot arms without needing dedicated sensors. We show that learned force signals enable force-feedback teleop on low-cost arms and improve BC policies. FACTR 2 consists of: 1. Neural External Torque (NEXT): learns external forces without needing dedicated force sensors. 2. Force-Informed Re-Sampling Training (FIRST): uses the learned force signal to identify task-critical regions and upsample them during training. w/ Steven Oh @ CoRL Tony Tao @ CoRL 🧵(1/N)

116,063 просмотров • 5 месяцев назад

Low-cost teleop systems have democratized robot data collection, but they lack any force feedback, making it challenging to teleoperate contact-rich tasks. Many robot arms provide force information — a critical yet underutilized modality in robot learning. We introduce: 1. 🦾A low-cost, force-feedback-enabled teleop system. 2. 🥊Force-Attending Curriculum Training (FACTR) uses force to improve generalization in complex, contact-rich tasks. 🧵(1/N)

Low-cost teleop systems have democratized robot data collection, but they lack any force feedback, making it challenging to teleoperate contact-rich tasks. Many robot arms provide force information — a critical yet underutilized modality in robot learning. We introduce: 1. 🦾A low-cost, force-feedback-enabled teleop system. 2. 🥊Force-Attending Curriculum Training (FACTR) uses force to improve generalization in complex, contact-rich tasks. 🧵(1/N)

Jason Liu ✈️ CoRL

153,214 просмотров • 1 год назад

Ever wish a robot could just move to any goal in any environment—avoiding all collisions and reacting in real time? 🚀Excited to share our #CoRL2025 paper, Deep Reactive Policy (DRP), a learning-based motion planner that navigates complex scenes with moving obstacles—directly from point cloud input. w/ Jiahui(Jim) Yang (1/N)

Ever wish a robot could just move to any goal in any environment—avoiding all collisions and reacting in real time? 🚀Excited to share our #CoRL2025 paper, Deep Reactive Policy (DRP), a learning-based motion planner that navigates complex scenes with moving obstacles—directly from point cloud input. w/ Jiahui(Jim) Yang (1/N)

72,517 просмотров • 1 год назад

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