Yingke Wang

Maryam Babaei, Yingke Wang, Hadrien Lautraite et al.

Counterfactuals are typically used in high-stakes decision areas to explain a machine learning model by showing how changes to the user profiles result in the desired outcome. However, explaining the model's decisions through counterfactuals can also be exploited by an adversary to conduct privacy attacks against the model or its training data. Drawing on the analogy that counterfactuals provide realistic substitutes for real training data, similar to synthetic data, we demonstrate in this paper how it is possible to successfully perform privacy attacks on counterfactuals by drawing on the attacks developed against synthetic data. More precisely, we investigate the effectiveness of the membership inference attacks designed for synthetic data on various types of counterfactuals. Additionally, while existing membership inference attacks against counterfactuals usually require to be able to query the model, we show how it is possible to perform successful membership inference attacks using only a set of counterfactuals, with no access to the model from which they are generated. Our results demonstrate that model developers should be more cautious when releasing counterfactuals to various users, as it can lead to a privacy breach.

Yingke Wang, Hao Li, Yifeng Zhu et al.

Robotic reproduction of oil paintings using soft brushes and pigments requires force-sensitive control of deformable tools, prediction of brushstroke effects, and multi-step stroke planning, often without human step-by-step demonstrations or faithful simulators. Given only a sequence of target oil painting images, can a robot infer and execute the stroke trajectories, forces, and colors needed to reproduce it? We present IMPASTO, a robotic oil-painting system that integrates learned pixel dynamics models with model-based planning. The dynamics models predict canvas updates from image observations and parameterized stroke actions; a receding-horizon model predictive control optimizer then plans trajectories and forces, while a force-sensitive controller executes strokes on a 7-DoF robot arm. IMPASTO integrates low-level force control, learned dynamics models, and high-level closed-loop planning, learns solely from robot self-play, and approximates human artists' single-stroke datasets and multi-stroke artworks, outperforming baselines in reproduction accuracy. Project website: https://impasto-robopainting.github.io/

Evans Han, Yunfan Jiang, Yingke Wang et al.

Recent advances in robot imitation learning have yielded powerful visuomotor policies capable of manipulating a wide variety of objects directly from monocular visual inputs. However, monocular observations inherently lack reliable depth cues and spatial awareness, which are critical for precise manipulation in cluttered or geometrically complex scenes. To address this limitation, we introduce StereoPolicy, a new visuomotor policy learning framework that directly leverages synchronized stereo image pairs to strengthen geometric reasoning, without requiring explicit 3D reconstruction or camera calibration. StereoPolicy employs pretrained 2D vision encoders to process each image independently and fuses the resulting representations through a Stereo Transformer. This design implicitly captures spatial correspondence and disparity cues. The framework integrates seamlessly with diffusion-based and pretrained vision-language-action (VLA) policies, delivering consistent improvements over RGB, RGB-D, point cloud, and multi-view baselines across three simulation benchmarks: RoboMimic, RoboCasa, and OmniGibson. We further validate StereoPolicy on real-robot experiments spanning both tabletop and bimanual mobile manipulation settings. Our results underscore stereo vision as a scalable and robust modality that bridges 2D pretrained representations with 3D geometric understanding for robotic manipulation.

Yihe Tang, Wenlong Huang, Yingke Wang et al.

Understanding fine-grained object affordances is imperative for robots to manipulate objects in unstructured environments given open-ended task instructions. However, existing methods of visual affordance predictions often rely on manually annotated data or conditions only on a predefined set of tasks. We introduce UAD (Unsupervised Affordance Distillation), a method for distilling affordance knowledge from foundation models into a task-conditioned affordance model without any manual annotations. By leveraging the complementary strengths of large vision models and vision-language models, UAD automatically annotates a large-scale dataset with detailed $<$instruction, visual affordance$>$ pairs. Training only a lightweight task-conditioned decoder atop frozen features, UAD exhibits notable generalization to in-the-wild robotic scenes and to various human activities, despite only being trained on rendered objects in simulation. Using affordance provided by UAD as the observation space, we show an imitation learning policy that demonstrates promising generalization to unseen object instances, object categories, and even variations in task instructions after training on as few as 10 demonstrations. Project website: https://unsup-affordance.github.io/

Tasha Kim, Yingke Wang, Hanvit Cho et al.

Neural Signal Operated Intelligent Robots (NOIR) system is a versatile brain-robot interface that allows humans to control robots for daily tasks using their brain signals. This interface utilizes electroencephalography (EEG) to translate human intentions regarding specific objects and desired actions directly into commands that robots can execute. We present NOIR 2.0, an enhanced version of NOIR. NOIR 2.0 includes faster and more accurate brain decoding algorithms, which reduce task completion time by 46%. NOIR 2.0 uses few-shot robot learning algorithms to adapt to individual users and predict their intentions. The new learning algorithms leverage foundation models for more sample-efficient learning and adaptation (15 demos vs. a single demo), significantly reducing overall human time by 65%.