​🍾 IROS 2026 x Saturday Robotics — Robotics Research Night | Reading Club 30. Pittsburgh 9/28

​📅 Monday, September 28
🕠 5:30 PM – 9:30 PM
📍 Pittsburgh, PA (walking distance from David L. Lawrence Convention Center)

​About Event

​After a full day of technical sessions at IROS 2026, join Saturday Robotics for an evening of high-signal technical discussions, lightning talks, and networking with researchers, founders, engineers, investors, and students building the future of robotics.

​Saturday Robotics has become one of Silicon Valley's largest community-driven robotics research groups, bringing together researchers from Google DeepMind, NVIDIA, Stanford, UC Berkeley, CMU, MIT, Physical Intelligence, Tesla, Figure, Agility Robotics, Skild AI, Boston Dynamics, and many leading robotics startups.

​Whether you're presenting at IROS, recruiting collaborators, building a startup, or simply interested in meeting others working on Physical AI, we'd love to see you in Pittsburgh.

​Agenda

​🕠 5:30 PM – 6:00 PM
Doors Open & Happy Hour Networking

​Grab a drink, meet fellow attendees, reconnect with old friends, and make new ones before the technical sessions begin.

​🕕 6:00 PM – 7:30 PM
Lightning Talk 1

​Lizhi(Gary) Yang is a Ph.D. candidate in Mechanical Engineering at Caltech, advised by Professor Aaron Ames in the AMBER Lab. Lizhi’s research focuses on humanoid robotics, robot safety, and learning-based control.

​Today, He will present PAC-MAN, a framework that combines onboard perception, reinforcement learning, and control barrier functions to teach humanoid robots to dodge incoming objects while staying balanced. The work explores how perception and safety must be designed together for fast, whole-body robot reactions.

​https://arxiv.org/abs/2607.28623v1

​PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball presents a perception-aware reinforcement learning framework designed to improve the safety and robustness of humanoid robots operating in dynamic environments. The work combines Control Barrier Functions (CBFs) with reinforcement learning and realistic onboard perception, addressing a key challenge in robot learning: policies can achieve impressive performance but may behave unsafely when exposed to unexpected disturbances or imperfect observations.

The system is demonstrated on a Unitree G1 humanoid robot performing a dynamic dodgeball task. During deployment, the robot relies only on proprioception and depth information from a head-mounted RGB-D camera. A segmentation model isolates the incoming ball from the depth image, creating a compact perception representation that closely matches the training setup. This design reduces the sim-to-real gap and allows the learned policy to operate without additional fine-tuning.

PAC-MAN introduces two levels of CBF-based safety guidance. Link-CBF represents collision clearance for every robot body link, rather than protecting only the torso. The safety constraint is incorporated into the training reward, encouraging the policy to learn collision-free behavior directly. Importantly, no runtime safety filter is required during deployment. Joint-CBF, meanwhile, provides stronger guidance through joint-space constraints and can serve as a privileged safety filter during training or evaluation, although its effectiveness depends strongly on accurate perception of the approaching object.

The framework also incorporates an adversarial human-motion prior, encouraging natural evasive behaviors such as ducking, sidestepping, leaning, and jumping. In real-world experiments, the G1 successfully dodged 19 of 20 throws (95%) with zero falls, while using onboard perception and no policy fine-tuning. The results demonstrate that integrating perception-aware safety constraints directly into robot learning can produce more robust, transferable, and deployment-ready whole-body behaviors for humanoid robots.

​Lightning Talk 2

​Aaron Li (Rhoda AI, Robot Data System Lead, Research Member of Technical Staff)

​How in-context learning is reshaping robot learning data at scale.

​Lightning Talk 3

​Lightning Talk 4

​Lightning Talk 5

​Lightning Talk 6

​We're opening a limited number of community lightning talk slots.

​~10 minutes each

• ​Research-focused and technical

• ​A few slides are sufficient

• ​Frontier papers, new projects, open problems, practical lessons, demos, or startup technical insights

​Topics include (but are not limited to):

• ​World Models

• ​Physical AI

• ​Humanoid Robotics

• ​Vision-Language-Action Models

• ​Robot Foundation Models

• ​Robot Learning

• ​Manipulation

• ​Reinforcement Learning

• ​Simulation & Sim-to-Real

• ​Spatial Intelligence

• ​Computer Vision

• ​Embodied AI

​📣 Call for Lightning Talks

​Interested in speaking?

​Please email junfanzhu98@gmail.com with:

• ​Talk title

• ​Abstract

• ​Name & affiliation

• ​Relevant links (arXiv paper, GitHub, slides, blog, etc.)

​Priority will be given to technically deep talks that encourage hot takes & discussion within the robotics research community.

​🕢 7:30 PM – 8:00 PM
Open Discussion & Paper Roundtable

​Bring a paper you'd like to highlight!

​We'll host informal roundtable discussions (approximately 10–20 minutes per topic) where participants can briefly introduce recent papers, explain why they matter, and discuss technical ideas, implementation details, open questions, and future directions.

​Everyone is welcome to start a discussion.

​🕗 8:00 PM – 9:30 PM
Happy Hour & Networking

​Continue conversations over food and drinks with fellow researchers, founders, students, and engineers.

​Who Should Join?

​Anyone attending IROS interested in:

• ​Physical AI

• ​World Models

• ​Humanoid Robotics

• ​Robot Learning

• ​Vision-Language-Action Models

• ​Embodied Foundation Models

• ​Manipulation

• ​Reinforcement Learning

• ​Simulation & Digital Twins

• ​Spatial Intelligence

​Whether this is your first Saturday Robotics event or you've attended one of our Reading Clubs before, everyone is welcome.

​Join the Community

​💬 Join our Discord Community
Join Discord Server

​𝕏 Follow Saturday Robotics on X
/saturdayrobotic

​⚠️ Space is limited. Please RSVP so we can estimate attendance.

​Looking forward to meeting everyone at IROS 2026 in Pittsburgh!