editor-in-chief of @berkeley_ai blog | cs phd @berkeley_ai ai robotics | former team lead @airacingtech | https://nitter.cf/t.co/7Mzu6UdTAu

Berkeley, CA
Joined May 2024
C.K. Wolfe retweeted
🎉 Workshop on Sim-to-Real-to-Field: The Science of Transfer is coming to #CoRL2026 in Austin! Our CFP is now open — with a $1,000 Best Paper Award generously sponsored by @tensrinc . 🔗 sim2real2field-corl26.github…
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Three data sources are popular for robot learning today (1) Humans tele-operating robots (2) Humans acting as humans doing tasks (3) Robots acting in the "safe space" of a simulator. Happy to share this work from UC Berkeley which shows the power of (2) combined with (3). Visual imitation followed by trial and error is a good recipe for robots as it is for human children. Whither tele-op?
One human demonstration. Any multi-fingered hand. Zero-shot sim-to-real visuomotor policy. morphometricimitation.github… Collaborators: @he_siming @ckwolfeofficial @HaozhiQ @LeaMue27 Shankar Sastry, Claire Tomlin, @JitendraMalikCV
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One human demonstration. Any multi-fingered hand. Zero-shot sim-to-real visuomotor policy. morphometricimitation.github… Collaborators: @he_siming @ckwolfeofficial @HaozhiQ @LeaMue27 Shankar Sastry, Claire Tomlin, @JitendraMalikCV
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C.K. Wolfe retweeted
We have a huge news to share today! Today we are unveiling the first truly accessible RL robot - welcome Microduck A 25 cm tiny open-source biped with 15 actuators and packed with sensors (camera, speaker, LiDAR, NFC, bluetooth, wifi, etc) that you train yourself with reinforcement learning. It's also playable out of the box with more than half a dozen fun and playful pre-trained policies to have it walk, sit, crouch, roller-skate, pick up objects with its articulated beak, and recover on its own. And all for less than $400. See all the details, play with the simulator and order it at: pollen-robotics.com/microduc… (video with sound on 🔊)
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C.K. Wolfe retweeted
Introducing S1, our new foundation model that learns from one example. It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning. Watch S1 operate in real-time via in-context learning:
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C.K. Wolfe retweeted
We can think of this as the robot analogue of RL for thinking, optimizing for good "thoughts" through trial-and-error. The surprising thing is that it's so fast, learning in under a hundred real-world trials. Website: semantic-action-rl.github.io… Paper: arxiv.org/abs/2606.31958
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Released on @berkeley_ai blog, new work from Jakob Bjorner, @a_lidayan, @satvikgolechha, @kartik_goyal_ & @alsuhr. As task horizons grow, LLM contexts can’t scale forever — ABBEL trains agents to maintain graded natural-language belief states instead of drowning in history … 🐻📄 bairblog.github.io/2026/07/2…
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C.K. Wolfe retweeted
Autoresearch just left the sandbox and entered the embodied world. We are excited to introduce 𝐄𝐍𝐏𝐈𝐑𝐄: a system that drops frontier coding agents onto a fleet of real robots and hands them the entire loop: reset the environment → search the literature → implement ideas and build the infra → train and deploy → self-verify → analyze the logs and rewrite the code → repeat, until the policy is reliable in the real world. No human in the loop. Guided only by the robot's self-proposed, heuristic-based success signal, the agents hill-climb to 99% on dexterous real-world tasks: organizing pins into a box, seating GPUs, tying zip-ties. We envision the bottleneck in robotics shifting — from building smarter algorithms to building the closed physical feedback loops an agent can finally turn on its own. 🔗 research.nvidia.com/labs/gea… From @NVIDIA @CMU_Robotics @Berkeley_AI 🧵
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Really excited to open source a new project: Omnigent, a meta-harness for AI agents. It lets you build multi-agent coding and custom agents, sitting above Claude Code, Codex, Pi, and agent SDKs to let you compose them. It also adds live collaboration and rich control policies.
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Good news at ICRA today! Really honored, and grateful to work with a fantastic team 🙂 More about the paper here: omniretarget.github.io/
OmniRetarget won the Best Conference Paper Award and Best Paper Award on Robot Manipulation and Locomotion at #ICRA2026! Really honored to see our work recognized :) Also thrilled to share that Perceptive Humanoid Parkour (PHP) has been accepted to #RSS2026, see you in Sydney!
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C.K. Wolfe retweeted
Can we build generalist robots with zero teleoperation? Come participate in the discussion and weigh in at our ICRA'26 workshop, BeyondTeleop, starting at 8.45 am CEST today (June 5th)! 📍 Strauss 3
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C.K. Wolfe retweeted
Longer chain-of-thought = slower inference, more context rot, and ballooning compute. So what if the model could decide for itself when to go parallel? Our new BAIR blog breaks down Adaptive Parallel Reasoning (APR) — the next paradigm in inference-time scaling. 🧵
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C.K. Wolfe retweeted
Berkeley CS Graduate Entrepreneurs (CSGE) is back with the annual Spring Mixer on May 8th! 🌉 Join us for a night where research meets startups, featuring an exciting panel with @sarahookr, @ericzelikman, and @NaveenGRao! RSVP early to save your spot: luma.com/nwca4b85
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We developed a simple, sample-efficient online RL technique for post-training image generation models. We see it as a possible steerable alternative to CFG, driven by any scalar reward, including human preference.
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Released on @berkeley_ai blog, recent work by @michaelpsenka M. Rabbat @ask1729 @ylecun @_amirbar — long horizons in visual world models punish naive gradients, GRASP reshapes them (lifted virtual states, noised state iterates, action-friendly descent) so planning stays stable when rollouts get ill-conditioned … 🐻📄 bair.berkeley.edu/blog/2026/…
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What’s the right representation for a world model? 3D, pixels, or something else? Excited to release our new paper “Forecasting Motion in the Wild” where we propose point tracks as tokens for generating complex non-rigid motion and behavior From @GoogleDeepmind @Berkeley_AI @TTIC_Connect
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C.K. Wolfe retweeted
Robotics: coding agents’ next frontier. So how good are they? We introduce CaP-X: an open-source framework and benchmark for coding agents, where they write code for robot perception and control, execute it on sim and real robots, observe the outcomes, and iteratively improve code reliability. From @NVIDIA @Berkeley_AI @CMU_Robotics @StanfordAILab capgym.github.io 🧵
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