"""Frozen SONIC v1.1 driving N robots at once from wrist targets. ``obs.pack_encoder`` builds one 1751-D vector per call from a full reference clip. An RL rollout needs something different: many environments, no clip, and a standing reference that never changes. Under those assumptions almost the whole encoder vector is constant, so it is built once here and only the heading (6) and the VR 3-point target (21) are rewritten each step. The history buffers and the push ordering mirror ``scripts/play_sonic.py`` exactly -- history holds post-physics proprio together with the action that produced it -- because SONIC is sensitive to that alignment. """ from __future__ import annotations from pathlib import Path import numpy as np from .constants import ( ACTION_CLIP, DECODER_DIM, DEFAULT_ANGLES, ENCODER_DIM, ENCODER_OFF, FUTURE_FRAMES, HIST_FRAMES, LOWER_BODY_ISAAC_IDX, MODE_IDS, NUM_JOINTS, TOKEN_DIM, ) from .math_np import heading_rel_6d from .policy import SonicOnnxAgent class BatchedSonicController: """Wrist targets in, 29 joint-position targets out, for N environments. Teleop mode against a fixed standing reference: the legs are told to hold the default stance, so whatever the base does is SONIC reacting to the arm rather than commanded locomotion. """ def __init__( self, checkpoint: str | Path, num_envs: int, *, agent: SonicOnnxAgent | None = None, ) -> None: self.num_envs = int(num_envs) self.agent = agent if agent is not None else SonicOnnxAgent(checkpoint, batched=True) self.default = np.asarray(DEFAULT_ANGLES, dtype=np.float64) self._enc = np.zeros((self.num_envs, ENCODER_DIM), dtype=np.float32) self._enc[:, ENCODER_OFF["encoder_mode_4"][0]] = float(MODE_IDS["teleop"]) # Constant standing reference: every future frame clamps to the same # pose, so the lower-body block is one row tiled over the 10 frames and # the matching velocity block stays zero. lower = self.default[LOWER_BODY_ISAAC_IDX].astype(np.float32) a, b = ENCODER_OFF["motion_joint_positions_lowerbody_10frame_step5"] self._enc[:, a:b] = np.tile(lower, FUTURE_FRAMES) self._dec = np.zeros((self.num_envs, DECODER_DIM), dtype=np.float32) self._hist = { "ang_vel": np.zeros((self.num_envs, HIST_FRAMES, 3), dtype=np.float32), "q_rel": np.zeros((self.num_envs, HIST_FRAMES, NUM_JOINTS), dtype=np.float32), "dq": np.zeros((self.num_envs, HIST_FRAMES, NUM_JOINTS), dtype=np.float32), "action": np.zeros((self.num_envs, HIST_FRAMES, NUM_JOINTS), dtype=np.float32), "gravity": np.zeros((self.num_envs, HIST_FRAMES, 3), dtype=np.float32), } self._last_action = np.zeros((self.num_envs, NUM_JOINTS), dtype=np.float32) self._primed = np.zeros(self.num_envs, dtype=bool) def reset(self, env_ids: np.ndarray | None = None) -> None: """Forget history for these environments; the next step re-primes them.""" if env_ids is None: self._primed[:] = False self._last_action[:] = 0.0 return idx = np.asarray(env_ids, dtype=np.int64).reshape(-1) if idx.size: self._primed[idx] = False self._last_action[idx] = 0.0 def step( self, *, joint_pos: np.ndarray, joint_vel: np.ndarray, ang_vel: np.ndarray, gravity: np.ndarray, root_quat_wxyz: np.ndarray, vr_pos: np.ndarray, vr_quat: np.ndarray, ) -> np.ndarray: """One 50 Hz control step for every environment. All arrays are batched over N. ``joint_pos`` / ``joint_vel`` are (N, 29) in Isaac Lab SONIC order, ``vr_pos`` is (N, 3, 3) and ``vr_quat`` (N, 3, 4 wxyz), both in the pelvis frame in left-wrist, right-wrist, torso order. Returns the (N, 29) clipped network action; the caller scales it into joint targets. """ q_rel = (np.asarray(joint_pos, dtype=np.float32) - self.default).astype(np.float32) dq = np.asarray(joint_vel, dtype=np.float32) ang = np.asarray(ang_vel, dtype=np.float32) grav = np.asarray(gravity, dtype=np.float32) self._record(ang, q_rel, dq, grav) quat = np.asarray(root_quat_wxyz, dtype=np.float64) ident = np.zeros_like(quat) ident[:, 0] = 1.0 a, b = ENCODER_OFF["motion_anchor_orientation_heading"] self._enc[:, a:b] = heading_rel_6d(quat, ident).astype(np.float32) a, b = ENCODER_OFF["vr_3point_local_target"] self._enc[:, a:b] = np.asarray(vr_pos, dtype=np.float32).reshape(self.num_envs, 9) a, b = ENCODER_OFF["vr_3point_local_orn_target"] self._enc[:, a:b] = np.asarray(vr_quat, dtype=np.float32).reshape(self.num_envs, 12) self._dec[:, :TOKEN_DIM] = self.agent.encode(self._enc) i = TOKEN_DIM # PolicyCfg term order: base_ang_vel, joint_pos, joint_vel, actions, gravity_dir. for key in ("ang_vel", "q_rel", "dq", "action", "gravity"): buf = self._hist[key] n = buf.shape[1] * buf.shape[2] self._dec[:, i : i + n] = buf.reshape(self.num_envs, n) i += n if i != DECODER_DIM: raise RuntimeError(f"decoder pack {i} != {DECODER_DIM}") action = np.clip(self.agent.decode(self._dec), -ACTION_CLIP, ACTION_CLIP) self._last_action = action.astype(np.float32) return self._last_action def joint_targets(self, action: np.ndarray, scale: np.ndarray) -> np.ndarray: """``default + scale * action`` for the 29 SONIC joints.""" return self.default + np.asarray(scale, dtype=np.float64) * np.asarray(action, dtype=np.float64) def _record( self, ang: np.ndarray, q_rel: np.ndarray, dq: np.ndarray, grav: np.ndarray ) -> None: """Append this frame, filling a freshly reset environment's whole window.""" frame = {"ang_vel": ang, "q_rel": q_rel, "dq": dq, "action": self._last_action, "gravity": grav} fresh = ~self._primed for key, buf in self._hist.items(): buf[:, :-1] = buf[:, 1:] buf[:, -1] = frame[key] if fresh.any(): buf[fresh] = frame[key][fresh][:, None, :] self._primed[:] = True