2026-07-30 · club pick

PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball

Lizhi Yang, Junheng Li, Aaron D. Ames

No peer-reviewed venue on record yet. 0 citations, as of the last refresh.

Abstract

We present PAC-MAN, a perception-aware CBF-RL framework that couples control-barrier safety with deployment-realistic onboard sensing for whole-body humanoid dodgeball. The deployed policy sees the ball only as segmentation-masked depth from a head-mounted camera, while training-time CBF guidance represents clearance to every body link, and an adversarial motion prior regularizes the resulting evasive reflexes. We evaluate on a controlled any-link contact benchmark with seeded throws in two regimes: single throws and a deployment loop in which the robot walks back to its station and recovers between throws. On this benchmark, the policy comes within a few points of a privileged state oracle: a fixed onboard camera alone is adequate for evasion. We find that usable barrier structure depends on perceptual observability: Joint-CBF gives the best performance with accurate ball states, degrades under fixed-camera observations when used only as training guidance, and recovers with a ball-tracking gimbal or privileged runtime filter. We therefore deploy a lightweight Link-CBF policy zero-shot on the Unitree G1 in the real world, where it tolerates imperfect perception, succeeds on 95% of throws, and uses semantic segmentation to dodge different balls.

arXiv comment: Website at https://lzyang2000.github.io/perceptive_cbf_rl/

Ten-minute slide kit

Six slides is the whole talk: what was broken, what people tried, what these authors did, what the numbers say, where it falls over, and the sentence people should remember.

SLIDE 1

The problem

Humanoid robots operating around moving objects must react within a fraction of a second, and the evasion itself must not cost them their balance. Dodgeball isolates this safety problem in a short-horizon, whole-body setting: the robot must perceive an incoming ball, determine which links are threatened, and coordinate the torso, arms, and legs quickly enough to avoid contact while remaining upright. Humans solve the same problem by keeping their eyes on the ball and moving their whole body out of its

SLIDE 2

What came before

Motion imitation and priors for humanoid control. Motion imitation has long provided a practical route to naturalistic, high-dimensional control. showed that RL can track reference motions reliably, replaced explicit tracking with an adversarial discriminator over motion features, which yields a style reward compatible with a task objective, and , learned reusable skill and style embeddings over large motion datasets. These build on a broader body of legged learning – trained from task rewards, command tracking,…

SLIDE 3

The method

Balls are thrown on an intermittent timer that leaves a recovery window between throws. The default distribution launches from a frontal cone (within ±25◦ of heading, 2-3 m ahead) with a flight time of roughly 0.6 s, which sets the reaction window; the aim point leads the robot's velocity. We mix two threat types equally so the policy learns both evasions: a descending ball that falls across the legs (sidestep/step-over) and a low-arc ball that rises to torso/head height (duck/lean). Throw heights are calibrated…

SLIDE 4

What they measured

<span id="page-4-0"></span>We first evaluate the PAC-MAN framework in simulation to quantify the impact of perception and safety structure; then we verify the full stack on the physical robot. Figure 4 shows emergent evasion modes from both. In simulation, we compare the onboard fixed-camera policy against a gimbal-mounted camera and a privileged state oracle while varying the safety structure at each perception level, from no barrier to the full joint-space CBF with its runtime filter. All policies are evaluated…

SLIDE 5

Where it breaks

We presented PAC-MAN, a perception-aware CBF-RL framework for whole-body humanoid dodgeball, and deployed its fixed-camera Link-CBF policy on the Unitree G1 using only onboard depth and proprioception. PAC-MAN couples segmentation-masked depth with controlbarrier guidance computed during training, and a human motion prior regularizes the form of the evasive reflexes. Our results show that perception-aware CBF-RL is a safetyinformation co-design problem: the appropriate barrier structure depends on the threat…

SLIDE 6

One-line takeaway

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.

Assembled from the paper's own PDF, parsed with its layout intact, 47,279 characters of it, then split on the paper's own section headings. Extractive, not generated: every sentence here is lifted from the paper. The takeaway line is the club's own one-liner from its reading list. Check it before you present it.

Presented at

Saturday Robotics, session 30

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

Pittsburgh, PA

Why the club picked it. 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.