ALT Human listeners can often identify the intended object several words before an instruction ends. Robots, however, are typically designed to wait until the speaker has finished before deciding how to act. We present EarlyCommit, an open-source tool that lets a robot commit to the intended object while the instruction is being spoken. After every recognized word, a fine-tuned text encoder scores each object in the scene. The robot acts when the calibrated probability of the best object reaches a threshold tau set by the user, so 1 - tau is the accepted risk of choosing the wrong object. Otherwise, it waits or asks. The tool includes a streaming speech front end, a scene and instruction generator with exact word-by-word ground truth, and scripts to train, calibrate, and evaluate. On instructions containing words never seen in training, EarlyCommit commits correctly 1.6 to 2 times as often as keyword matching, is right at least 92 percent of the time when it commits at tau = 0.9, and needs

ALT Learning a collective policy for state-based Cartesian manipulation across diverse robotic embodiments presents a fundamental challenge due to embodiment-dependent transition dynamics. Collective learning aggregates multi-robot experience to discover shared task structure, yet naive aggregation can average incompatible action modes when different embodiments realize the same Cartesian command through different transition behavior. This leads to strategy ambiguity and performance collapse on dynamically outlying embodiments. To address this, we propose PACE, a two-stage collective learning framework that separates shared task-space intent from embodiment-dependent transition dynamics. The first stage employs a representation layer pre-trained on offline trajectories to extract task-level context from collective data. The second stage introduces a decision layer equipped with a predictive dynamics adapter. By forcing the latent state to predict future transitions, this adapter acts as a

ALT Learning to predict how the world evolves can provide vision-language-action (VLA) policies with predictive context for long-horizon control, but its effectiveness depends on what future representation is modeled and how it conditions action generation. We introduce PLaW-VLA, which models task-relevant future states in a pretrained prediction-oriented representation space, reducing the need to predict control-irrelevant visual details. Built on a Mixture-of-Transformers architecture, PLaW-VLA conditions action generation on observation history, current task semantics, and predicted future states through structured causal attention. Experiments show a +11.8 percentage-point (pp) gain over reactive policies on RoboTwin Hard Horizon III and a +1.77 pp gain over reconstruction-oriented latent prediction on zero-shot LIBERO-Plus, supporting improved long-horizon control and generalization under distribution shift, respectively. By avoiding low-level visual reconstruction, PLaW-VLA lowers th