Spaces:
Sleeping
Sleeping
| # Copyright (c) 2022 NVIDIA CORPORATION. All rights reserved. | |
| # NVIDIA CORPORATION and its licensors retain all intellectual property | |
| # and proprietary rights in and to this software, related documentation | |
| # and any modifications thereto. Any use, reproduction, disclosure or | |
| # distribution of this software and related documentation without an express | |
| # license agreement from NVIDIA CORPORATION is strictly prohibited. | |
| ########################################################################### | |
| # Example Sim Quadruped | |
| # | |
| # Shows how to set up a simulation of a rigid-body quadruped articulation | |
| # from a URDF using the wp.sim.ModelBuilder(). | |
| # Note this example does not include a trained policy. | |
| # | |
| ########################################################################### | |
| import math | |
| import os | |
| import numpy as np | |
| import warp as wp | |
| import warp.sim | |
| import warp.sim.render | |
| wp.init() | |
| # Taken from env/environment.py | |
| def compute_env_offsets(num_envs, env_offset=(5.0, 0.0, 5.0), up_axis="Y"): | |
| # compute positional offsets per environment | |
| env_offset = np.array(env_offset) | |
| nonzeros = np.nonzero(env_offset)[0] | |
| num_dim = nonzeros.shape[0] | |
| if num_dim > 0: | |
| side_length = int(np.ceil(num_envs ** (1.0 / num_dim))) | |
| env_offsets = [] | |
| else: | |
| env_offsets = np.zeros((num_envs, 3)) | |
| if num_dim == 1: | |
| for i in range(num_envs): | |
| env_offsets.append(i * env_offset) | |
| elif num_dim == 2: | |
| for i in range(num_envs): | |
| d0 = i // side_length | |
| d1 = i % side_length | |
| offset = np.zeros(3) | |
| offset[nonzeros[0]] = d0 * env_offset[nonzeros[0]] | |
| offset[nonzeros[1]] = d1 * env_offset[nonzeros[1]] | |
| env_offsets.append(offset) | |
| elif num_dim == 3: | |
| for i in range(num_envs): | |
| d0 = i // (side_length * side_length) | |
| d1 = (i // side_length) % side_length | |
| d2 = i % side_length | |
| offset = np.zeros(3) | |
| offset[0] = d0 * env_offset[0] | |
| offset[1] = d1 * env_offset[1] | |
| offset[2] = d2 * env_offset[2] | |
| env_offsets.append(offset) | |
| env_offsets = np.array(env_offsets) | |
| min_offsets = np.min(env_offsets, axis=0) | |
| correction = min_offsets + (np.max(env_offsets, axis=0) - min_offsets) / 2.0 | |
| if isinstance(up_axis, str): | |
| up_axis = "XYZ".index(up_axis.upper()) | |
| correction[up_axis] = 0.0 # ensure the envs are not shifted below the ground plane | |
| env_offsets -= correction | |
| return env_offsets | |
| class Example: | |
| def __init__(self, stage=None, num_envs=1, enable_rendering=True, print_timers=True): | |
| self.device = wp.get_device() | |
| self.num_envs = num_envs | |
| articulation_builder = wp.sim.ModelBuilder() | |
| wp.sim.parse_urdf( | |
| os.path.join(os.path.dirname(__file__), "assets/quadruped.urdf"), | |
| articulation_builder, | |
| xform=wp.transform([0.0, 0.7, 0.0], wp.quat_from_axis_angle(wp.vec3(1.0, 0.0, 0.0), -math.pi * 0.5)), | |
| floating=True, | |
| density=1000, | |
| armature=0.01, | |
| stiffness=120, | |
| damping=1, | |
| shape_ke=1.0e4, | |
| shape_kd=1.0e2, | |
| shape_kf=1.0e2, | |
| shape_mu=0.0, | |
| limit_ke=1.0e4, | |
| limit_kd=1.0e1, | |
| ) | |
| builder = wp.sim.ModelBuilder() | |
| self.sim_time = 0.0 | |
| self.frame_dt = 1.0 / 100.0 | |
| episode_duration = 5.0 # seconds | |
| self.episode_frames = int(episode_duration / self.frame_dt) | |
| self.sim_substeps = 5 | |
| self.sim_dt = self.frame_dt / self.sim_substeps | |
| offsets = compute_env_offsets(num_envs) | |
| for i in range(num_envs): | |
| builder.add_builder(articulation_builder, xform=wp.transform(offsets[i], wp.quat_identity())) | |
| builder.joint_q[-12:] = [0.2, 0.4, -0.6, -0.2, -0.4, 0.6, -0.2, 0.4, -0.6, 0.2, -0.4, 0.6] | |
| builder.joint_target[-12:] = [0.2, 0.4, -0.6, -0.2, -0.4, 0.6, -0.2, 0.4, -0.6, 0.2, -0.4, 0.6] | |
| np.set_printoptions(suppress=True) | |
| # finalize model | |
| self.model = builder.finalize() | |
| self.model.ground = True | |
| self.model.joint_attach_ke = 16000.0 | |
| self.model.joint_attach_kd = 200.0 | |
| self.integrator = wp.sim.XPBDIntegrator() | |
| self.enable_rendering = enable_rendering | |
| self.renderer = None | |
| if self.enable_rendering: | |
| self.renderer = wp.sim.render.SimRenderer(self.model, stage) | |
| self.print_timers = print_timers | |
| self.state_0 = self.model.state() | |
| self.state_1 = self.model.state() | |
| wp.sim.eval_fk(self.model, self.model.joint_q, self.model.joint_qd, None, self.state_0) | |
| self.use_graph = wp.get_device().is_cuda | |
| self.graph = None | |
| if self.use_graph: | |
| # create update graph | |
| wp.capture_begin(self.device) | |
| try: | |
| self.update() | |
| finally: | |
| self.graph = wp.capture_end(self.device) | |
| def update(self): | |
| with wp.ScopedTimer("simulate", active=True, print=self.print_timers): | |
| if not self.use_graph or self.graph is None: | |
| for _ in range(self.sim_substeps): | |
| self.state_0.clear_forces() | |
| wp.sim.collide(self.model, self.state_0) | |
| self.integrator.simulate(self.model, self.state_0, self.state_1, self.sim_dt) | |
| self.state_0, self.state_1 = self.state_1, self.state_0 | |
| else: | |
| wp.capture_launch(self.graph) | |
| if not wp.get_device().is_capturing: | |
| self.sim_time += self.frame_dt | |
| def render(self, is_live=False): | |
| if self.enable_rendering: | |
| with wp.ScopedTimer("render", active=True, print=self.print_timers): | |
| time = 0.0 if is_live else self.sim_time | |
| self.renderer.begin_frame(time) | |
| self.renderer.render(self.state_0) | |
| self.renderer.end_frame() | |
| def run(self): | |
| profiler = {} | |
| with wp.ScopedTimer("simulate", detailed=False, print=False, active=True, dict=profiler): | |
| for _ in range(self.episode_frames): | |
| self.update() | |
| self.render() | |
| wp.synchronize() | |
| if self.enable_rendering: | |
| self.renderer.save() | |
| avg_time = np.array(profiler["simulate"]).mean() / self.episode_frames | |
| avg_steps_second = 1000.0 * float(self.num_envs) / avg_time | |
| print(f"envs: {self.num_envs} steps/second {avg_steps_second} avg_time {avg_time}") | |
| return 1000.0 * float(self.num_envs) / avg_time | |
| if __name__ == "__main__": | |
| profile = False | |
| if profile: | |
| env_count = 2 | |
| env_times = [] | |
| env_size = [] | |
| for i in range(15): | |
| example = Example(num_envs=env_count, enable_rendering=False, print_timers=False) | |
| steps_per_second = example.run() | |
| env_size.append(env_count) | |
| env_times.append(steps_per_second) | |
| env_count *= 2 | |
| # dump times | |
| for i in range(len(env_times)): | |
| print(f"envs: {env_size[i]} steps/second: {env_times[i]}") | |
| # plot | |
| import matplotlib.pyplot as plt | |
| plt.figure(1) | |
| plt.plot(env_size, env_times) | |
| plt.xscale("log") | |
| plt.xlabel("Number of Envs") | |
| plt.yscale("log") | |
| plt.ylabel("Steps/Second") | |
| plt.show() | |
| else: | |
| stage = os.path.join(os.path.dirname(__file__), "outputs/example_sim_quadruped.usd") | |
| example = Example(stage, num_envs=25, enable_rendering=True) | |
| example.run() | |