TRACE: Delay-Adaptive Temporal Residuals for Asynchronous Robotic Control

Asynchronous inference supports real-time action execution and improves control responsiveness by allowing the robot to continue acting while the next action sequence is generated. However, overlapping inference with execution cannot eliminate computational latency: the robot and environment may evolve before predicted actions take effect, creating an observation-to-execution mismatch that requires correction. We introduce TRACE (Temporal Residual Adaptation for Asynchronous Control with Frozen Experts), a temporal residual injection framework for frozen flow-based action generators. Its central mechanism compresses causal observation–command history and committed actions into delay-conditioned context, then injects this context into internal action representations through gated cross-attention. TRACE preserves the backbone's original modules and pretrained parameters while learning an additional temporal residual pathway. Its injection mechanism is orthogonal in design to delay-aware policy training and sampling-time control, enabling complementary asynchronous enhancements around a frozen backbone. Experiments on Kinetix and LIBERO show aggregate success gains over naive asynchronous execution for the initial adapted configurations. An independent Kinetix evaluation further shows improved aggregate success with the calibrated deployment configuration.