Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robot learning remains one of the most promising directions for enhancing thecapabilities ofrobots.

Puck detection in ice hockey broadcast videos poses significant challenges due to the puck's small size, frequent occlusions, motion blur, broadcast artifacts, and scale inconsistencies due to varying camera zoom and broadcast camera viewpoints. Prior works focus on appearance-based or motion-based cues of the puck without explicitly modelling the cues derived from player behaviour . Players consistently turn their bodies and direct their gaze toward the puck. Motivated by this strong contextual cue, we propose Puck Localization Using Contextual Cues (PLUCC), a novel approach for scale-aware and context-driven single-frame puck detections. PLUCC consists of three components: (a) a contextual encoder, which utilizes player orientations and positioning as helpful priors; (b) a feature pyramid encoder, which extracts multiscale features from the dual encoders; and (c) a gating decoder that combines latent features with a channel gating mechanism. F or evaluation, in addition to standard average precision, we propose Rink Space Localization Error (RSLE), a scale-invariant homography-based metric for removing perspective bias from rink space evaluation. The experimental results of PLUCC on the PuckDataset dataset demonstrated state-of-the-art detection performance, surpassing previous baseline methods by an average precision improvement of 12.2% and RSLE average precision of 25%. Our research demonstrates the critical role of contextual understanding in improving puck detection performance, with broad implications for automated sports analysis.

Learning from humans is challenging because people are imperfect teachers. When everyday humans show the robot a new task they want it to perform, humans inevitably make errors (e.g., inputting noisy actions) and provide suboptimal examples (e.g., overshooting the goal). Existing methods learn by mimicking the exact behaviors the human teacher provides -- but this approach is fundamentally limited because the demonstrations themselves are imperfect. In this work we advance offline imitation learning by enabling robots to extrapolate what the human teacher meant, instead of only considering what the human actually showed. We achieve this by hypothesizing that all of the human's demonstrations are trying to convey a single, consistent policy, while the noise and sub-optimality within their behaviors obfuscates the data and introduces unintentional complexity. To recover the underlying policy and learn what the human teacher meant, we introduce Counter-BC, a generalized version of behavior cloning. Counter-BC expands the given dataset to include actions close to behaviors the human demonstrated (i.e., counterfactual actions that the human teacher could have intended, but did not actually show). During training Counter-BC autonomously modifies the human's demonstrations within this expanded region to reach a simple and consistent policy that explains the underlying trends in the human's dataset. Theoretically, we prove that Counter-BC can extract the desired policy from imperfect data, multiple users, and teachers of varying skill levels. Empirically, we compare Counter-BC to state-of-the-art alternatives in simulated and real-world settings with noisy demonstrations, standardized datasets, and real human teachers. See videos of our work here: https://youtu.be/XaeOZWhTt68

The latest season of Black Mirror feels almost therapeutic as we peer over the cliff of civilizational collapse. Everything is awful, but at least we don't have to worry about renting out access to our brains from skeevy startups, or dealing with the consequences of a PC game's super-intelligent AI. While Black Mirror felt like a horrifying harbinger of an over-teched future when it debuted in 2011, now it's practically an escape from the fresh hell of real world headlines. That's not to say that the show has lost any of the acerbic bite from creator Charlie Brooker. But now Brooker and his writers -- Ms. Marvel showrunner Bisha K. Ali, William Bridges, Ella Road and Bekka Bowling -- more deftly wield their talent for cultural analysis. Not all of the new episodes revolve around nefarious new tech, sometimes the tools themselves are genuinely helpful -- it's humans who are often the real problem.

Shadow Labyrinth, an utterly bonkers riff on Pac-Man and sidescrolling Metroidvania games, will hit digital store shelves on July 18. It'll be available for Nintendo Switch, PC via Steam, PS5 and Xbox Series X/S. The game casts players as Swordsman No. 8 as he befriends a yellow orb called Puck. For the gaming historians out there, Puck-Man was the original name for Pac-Man. The gameplay involves switching from the classic sword-wielding hero to Puck, with the latter able to crawl on walls and (surprise) gobble up yellow dots.

OnePlus has been a bit up and down since it merged with Oppo back in 2021. It gained greater access to powerful components and partnerships with brands like Hasselblad, while its software and product lineup took a few steps back before finding its stride again. But now, three generations after the merger, OnePlus' latest flagship phone -- the OnePlus 13 -- feels like a fantastic return to form. In some areas, the company is even pushing the limits of hardware and gadget design in ways that rivals from Samsung and Google aren't. And with a starting price of 900, OnePlus has managed to undercut its closest competitor too, which makes this phone a great choice for anyone who cares more about getting hardware upgrades than fancy new AI tricks.

Many continuous control problems can be formulated as sparse-reward reinforcement learning (RL) tasks. In principle, online RL methods can automatically explore the state space to solve each new task. However, discovering sequences of actions that lead to a non-zero reward becomes exponentially more difficult as the task horizon increases. Manually shaping rewards can accelerate learning for a fixed task, but it is an arduous process that must be repeated for each new environment. We introduce a systematic reward-shaping framework that distills the information contained in 1) a task-agnostic prior data set and 2) a small number of task-specific expert demonstrations, and then uses these priors to synthesize dense dynamics-aware rewards for the given task. This supervision substantially accelerates learning in our experiments, and we provide analysis demonstrating how the approach can effectively guide online learning agents to faraway goals. Keywords: Imitation Learning, Learning from Demonstrations, Reward Shaping.

The same phenomena that let beetles float across ponds and cause Cheerios to cluster together in your cereal bowl can be harnessed to make tiny floating robots. One of these, the Marangoni effect, arises when a fluid with a lower surface tension rapidly spreads out across the surface of a fluid with higher surface tension. This effect is exploited by Stenus beetles, which have evolved to zip across ponds by secreting a substance called stenusin, as well as soap-powered toy boats. To investigate how this could be used by engineers, Jackson Wilt at Harvard University and his colleagues 3D-printed round, plastic pucks around a centimetre in diameter. Inside each was an air chamber for buoyancy and a tiny fuel tank containing alcohol, which has a lower surface tension than water, in concentrations from 10 to 50 per cent.

Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robot learning remains one of the most promising directions for enhancing the capabilities of robots. When deploying learning-based approaches on real robots, extra effort is required to address the challenges posed by various real-world factors. To investigate the key factors influencing real-world deployment and to encourage original solutions from different researchers, we organized the Robot Air Hockey Challenge at the NeurIPS 2023 conference. We selected the air hockey task as a benchmark, encompassing low-level robotics problems and high-level tactics. Different from other machine learning-centric benchmarks, participants need to tackle practical challenges in robotics, such as the sim-to-real gap, low-level control issues, safety problems, real-time requirements, and the limited availability of real-world data. Furthermore, we focus on a dynamic environment, removing the typical assumption of quasi-static motions of other real-world benchmarks. The competition's results show that solutions combining learning-based approaches with prior knowledge outperform those relying solely on data when real-world deployment is challenging. Our ablation study reveals which real-world factors may be overlooked when building a learning-based solution. The successful real-world air hockey deployment of best-performing agents sets the foundation for future competitions and follow-up research directions.