A fine-tuned Vision-Language-Action (VLA) policy can easily place the alphabet soup in the basket on command, just as it saw during fine-tuning. However, if the basket is shifted five centimeters to the left, it drops the soup off the table. We developed an interpretability pipeline to investigate what such a policy is computing. Injecting task A's activations into the action expert while the policy looks at task B's scene makes $π_{0.5}$ execute task A's motor trajectory in 99.6\% of episodes ($n{=}1{,}968$). The injected program is bound to absolute workspace coordinates and ignores the visible scene, which provides a mechanistic account of the perturbation brittleness that concurrent benchmarks report. The binding holds on a physical UR10e: directed replay redirects the commanded task in 26/26 rollouts in one direction and 18/26 in the other, with no-injection and random-direction controls at the floor, and once the source object is moved 10-15 cm the arm grasps and releases at the recorded coordinates in 50/50 trials, picking up the wrong block when one sits there and closing on empty air when none does. Applying the same interventions to five VLAs on LIBERO, MetaWorld, SimplerEnv, and ALOHA over $420{,}000+$ rollouts yields three further findings: every architecture encodes the instruction, and four of the five weight it below vision whenever the scene alone identifies the goal; under the standard replacement write-back the SAE pooling that preserves behavior is architecture-specific ($π_{0.5}$ per-token, SmolVLA indifferent), and goal selection and motor generation separate into VLM and expert pathways wherever the two are distinguishable. We release \textbf{Action Atlas} (https://action-atlas.com), an interactive platform covering all 520+ trained SAEs, 79 identified concepts, and the scored UR10e rollouts. Code: https://cwru-aism.github.io/vla-interp-page/
Changes to what a robot can see do not mean that there is a change in the problem it is trying to solve, and many language-conditioned manipulation systems struggle with this distinction. When something in a scene moves, the robot might need to execute its actions in different places, but its manipulation plan can stay the same. We propose Sequential Planning via Anchored Robotic Keypoints (SPARK), which describes a task as a symbolic behavior tree that we anchor to the current 3D observations when each action is called. By verifying the plan at runtime we can recover from failures locally and anchor the objects to the scene again. We ask for a new plan only if this fails. We isolate this concept by taking a fixed plan and moving objects in the scene while executing. When displacing objects by $10$\,cm we were still able to complete $66.7\%$ of trials with scene-diff verification and layered recovery, and we did not have to change the plan. Without practicing on the tasks being tested or using ground-truth object poses from the simulator during rollouts, we test our full system (with one open-weight 27B planner with 4-bit weights) on LIBERO-PRO. Under this protocol SPARK achieves a mean success of $75.1\%$ across the 6 position and task settings and $69.7\%$ on the held-out language and object settings. We also achieve a $68\%$ success rate across 11 physical task-embodiment settings on 3 different robots. Our work demonstrates that for an important class of failures, the robot does not need to ask for a new plan for its task. It can be enough to update the system's view of the world. Code and videos can be found at https://cwru-aism.github.io/spark-page/
This paper introduces Quaternion Approximate Networks (QUAN), a novel deep learning framework that leverages quaternion algebra for rotation equivariant image classification and object detection. Unlike conventional quaternion neural networks attempting to operate entirely in the quaternion domain, QUAN approximates quaternion convolution through Hamilton product decomposition using real-valued operations. This approach preserves geometric properties while enabling efficient implementation with custom CUDA kernels. We introduce Independent Quaternion Batch Normalization (IQBN) for training stability and extend quaternion operations to spatial attention mechanisms. QUAN is evaluated on image classification (CIFAR-10/100, ImageNet), object detection (COCO, DOTA), and robotic perception tasks. In classification tasks, QUAN achieves higher accuracy with fewer parameters and faster convergence compared to existing convolution and quaternion-based models. For objection detection, QUAN demonstrates improved parameter efficiency and rotation handling over standard Convolutional Neural Networks (CNNs) while establishing the SOTA for quaternion CNNs in this downstream task. These results highlight its potential for deployment in resource-constrained robotic systems requiring rotation-aware perception and application in other domains. Code: https://cwru-aism.github.io/ QUANpaper/