The next robotic foundation model may not be a model. It may be an autonomous self-improvement learning system. Recent work across @physical_int and @NVIDIARobotics GEAR reveals something more structural than stronger policies. It reveals a shift in what counts as the unit of intelligence in robotics. 1️⃣ Context Conditioning Layer — multimodal context (language, visual subgoals, metadata) that makes policies steerable instead of rigidly task-specified. 2️⃣ Foundation Policy (@physical_int π0.7—future) — a context-conditioned VLA whose steerable prompting produces language coaching and zero-shot cross-embodiment generalization by treating rich context as controllable input. 3️⃣ Skill Acquisition (@nvidia CHORD) — human demonstrations are transformed into object-centric contact-wrench trajectories. The system learns to instantiate the dynamics that move objects rather than imitate human motion. On 1,831 long-horizon tasks this yields 82% success and enables whole-body transfer from hand-only data. The change is not better imitation; it is a change in what is being imitated. 4️⃣ Skill Memory (@nvidia ASPIRE) — evolutionary search over executable control programs, debugged via multimodal traces and distilled into a reusable library of sensorimotor code. Memory now takes the form of programs that can be retrieved and composed, not weights that must be retrained. 5️⃣ Autonomous Research (@nvidia ENPIRE) — coding agents close a physical feedback loop in which they reset scenes, run policies on real robot fleets, analyze results, rewrite code, and iterate. The optimizer is no longer gradient descent on a fixed loss. It is autonomous experimentation that continuously rewrites both the policy and the training process itself. Robotics here is no longer model-centric; it is loop-centric. Our @saturdayrobotic Robotics & World Models Reading Club will delve deep into technical details of NVIDIA #ENPIRE on July 25, stay tuned 👉🏻 RSVP: luma.com/5ltk12w5. 6️⃣ Fleet-Scale Feedback — parallel physical interaction makes robot-hours a first-class scaling dimension. Static datasets are no longer the dominant source of supervision. The physical world becomes a continuously self-generating training signal. The shift is not that these components are becoming better aligned. It is that optimization, memory, representation, and control are collapsing into the same closed loop. The model is no longer the unit of intelligence. The loop is. Foundation models compressed intelligence into weights. Agentic robotics is decompressing it into steerable context, object-centric dynamics, executable programs, autonomous experimentation, and continuous physical feedback. Robotics is shifting from learning policies to building learning systems whose intelligence grows through self-modifying interaction with the world. The first generation of robot foundation models learned from datasets. The next generation may learn from running labs.