gym_genesis/tasks/cube

import genesis as gs import numpy as np from gymnasium import spaces import random import torch joints_name = ( "joint1", "joint2", "joint3", "joint4", "joint5", "joint6", "joint7", "finger_joint1", "finger_joint2", ) AGENT_DIM = len(joints_name) ENV_DIM = 11 class CubePick: def __init__(self, enable_pixels, observation_height, observation_width, num_envs, env_spacing, camera_capture_mode, strip_environment_state): self.enable_pixels = enable_pixels self.observation_height = observation_height self.observation_width = observation_width self.num_envs = num_envs self._random = np.random.RandomState() self._build_scene(num_envs, env_spacing) self.observation_space = self._make_obs_space() self.action_space = spaces.Box(low=-1.0, high=1.0, shape=(AGENT_DIM,), dtype=np.float32) self.camera_capture_mode = camera_capture_mode self.strip_environment_state=strip_environment_state def _build_scene(self, num_envs, env_spacing): if not gs._initialized: gs.init(backend=gs.gpu, precision="32") self.scene = gs.Scene( viewer_options=gs.options.ViewerOptions( camera_pos=(3, -1, 1.5), camera_lookat=(0.0, 0.0, 0.5), camera_fov=30, res=(self.observation_width, self.observation_height), max_FPS=60, ), sim_options=gs.options.SimOptions(dt=0.01), rigid_options=gs.options.RigidOptions(box_box_detection=True), show_viewer=False, ) self.plane = self.scene.add_entity(gs.morphs.Plane()) self.franka = self.scene.add_entity(gs.morphs.MJCF(file="xml/franka_emika_panda/panda.xml")) self.cube = self.scene.add_entity( gs.morphs.Box(size=(0.04, 0.04, 0.04), pos=(0.65, 0.0, 0.02)) ) if self.enable_pixels: self.cam = self.scene.add_camera( res=(self.observation_width, self.observation_height), pos=(3.5, 0.0, 2.5), lookat=(0, 0, 0.5), fov=30, GUI=False ) self.scene.build(n_envs=num_envs, env_spacing=env_spacing) self.motors_dof = np.arange(7) self.fingers_dof = np.arange(7, 9) self.eef = self.franka.get_link("hand") def _make_obs_space(self): if self.enable_pixels: # we explicity remove the need of environment_state return spaces.Dict({ "agent_pos": spaces.Box(low=-np.inf, high=np.inf, shape=(AGENT_DIM,), dtype=np.float32), "pixels": spaces.Box(low=0, high=255, shape=(self.observation_height, self.observation_width, 3), dtype=np.uint8), }) else: return spaces.Dict({ "agent_pos": spaces.Box(low=-np.inf, high=np.inf, shape=(AGENT_DIM,), dtype=np.float32), "environment_state": spaces.Box(low=-np.inf, high=np.inf, shape=(ENV_DIM,), dtype=np.float32), }) def reset(self): B = self.num_envs # === Deterministic cube spawn using task._random === x = self._random.uniform(0.45, 0.80, size=(B,)) y = self._random.uniform(-0.25, 0.25, size=(B,)) z = np.full((B,), 0.02) pos_tensor = torch.tensor(np.stack([x, y, z], axis=1), dtype=torch.float32, device=gs.device) quat_tensor = torch.tensor([[0, 0, 0, 1]] * B, dtype=torch.float32, device=gs.device) self.cube.set_pos(pos_tensor) self.cube.set_quat(quat_tensor) # Reset Franka to home position qpos = np.array([0.0, -0.4, 0.0, -2.2, 0.0, 2.0, 0.8, 0.04, 0.04]) qpos_tensor = torch.tensor(qpos, dtype=torch.float32, device=gs.device).repeat(B, 1) self.franka.set_qpos(qpos_tensor, zero_velocity=True) self.franka.control_dofs_position(qpos_tensor[:, :7], self.motors_dof) self.franka.control_dofs_position(qpos_tensor[:, 7:], self.fingers_dof) self.scene.step() if self.enable_pixels: self.cam.start_recording() return self.get_obs() def seed(self, seed): np.random.seed(seed) random.seed(seed) self._random = np.random.RandomState(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) self.action_space.seed(seed) def step(self, action): self.franka.control_dofs_position(action[:, :7], self.motors_dof) self.franka.control_dofs_position(action[:, 7:], self.fingers_dof) self.scene.step() reward = self.compute_reward() obs = self.get_obs() return None, reward, None, obs def compute_reward(self): # Get z positions of cube in each env z = self.cube.get_pos().cpu().numpy() # shape: (B, 3) z_height = z[:, -1] # get the z (height) coordinate for each env reward = (z_height > 0.1).astype(np.float32) # shape: (B,) return reward def get_obs(self): # (B, X) # === agent (robot) state features === eef_pos = self.eef.get_pos() # (B, 3) eef_rot = self.eef.get_quat() # (B, 4) gripper = self.franka.get_dofs_position()[..., 7:9] # (B, 2) # === environment (object) state features === cube_pos = self.cube.get_pos() # (B, 3) cube_rot = self.cube.get_quat() # (B, 4) diff = eef_pos - cube_pos # (B, 3) (privileged) dist = torch.norm(diff, dim=1, keepdim=True) # (B, 1) (privileged) # compose observation dicts agent_pos = torch.cat([eef_pos, eef_rot, gripper], dim=1).float() # (B, 9) environment_state = torch.cat([cube_pos, cube_rot, diff, dist], dim=1).float() # (B, 11) obs = { "agent_pos": agent_pos, # (B, 9) "environment_state": environment_state, # (B, 11) } if self.enable_pixels: #TODO (jadechoghari): it's hacky but keep it for the sake of saving time if self.strip_environment_state is True: del obs["environment_state"] if self.camera_capture_mode == "per_env": # Capture a separate image for each environment batch_imgs = [] for i in range(self.num_envs): pos_i = self.scene.envs_offset[i] + np.array([3.5, 0.0, 2.5]) lookat_i = self.scene.envs_offset[i] + np.array([0, 0, 0.5]) self.cam.set_pose(pos=pos_i, lookat=lookat_i) img = self.cam.render()[0] batch_imgs.append(img) pixels = np.stack(batch_imgs, axis=0) # shape: (B, H, W, 3) assert pixels.ndim == 4, f"pixels shape {pixels.shape} is not 4D (B, H, W, 3)" elif self.camera_capture_mode == "global": # Capture a single global/overview image pixels = self.cam.render()[0] # shape: (H, W, 3) assert pixels.ndim == 3, f"pixels shape {pixels.shape} is not 3D (H, W, 3)" else: raise ValueError(f"Unknown camera_capture_mode: {self.camera_capture_mode}") obs["pixels"] = pixels return obs

gym_genesis/tasks/cube_pick.py (144 lines of code) (raw):