Change-the-type-of-dexterous-hand.md

How to change the type of dexterous hand

The default dexterous hand used by Bi-DexHands framework is the Shadow Hand. We know there is more type of dexterous hands than the shadow hand like allegro hand, trifinger, et al, and supporting other dexterous hands helps to advance research and community development.

We provide an example for using allegro hand to complete Hand Over task. Pass --task=AllegroHandOver --algo=ppoto use this environment.

A brief introduction to the main modification

Prepare the urdf/mjcf model file for the new dexterous hand and import it into the environment:

allegro_hand_asset_file = "urdf/allegro_hand_model/urdf/allegro_hand_r.urdf"
allegro_hand_another_asset_file = "urdf/allegro_hand_model/urdf/allegro_hand_r.urdf"

Modify the action dimension:

if self.is_multi_agent:
    self.num_agents = 2
    self.cfg["env"]["numActions"] = 16
    
else:
    self.num_agents = 1
    self.cfg["env"]["numActions"] = 32

DoF-related dimensions in observation:

self.obs_buf[:, action_obs_start:action_obs_start + 16] = self.actions[:, :16]

# another_hand
another_hand_start = action_obs_start + 16
...
self.obs_buf[:, action_another_obs_start:action_another_obs_start + 16] = self.actions[:, 16:]

obj_obs_start = action_another_obs_start + 16  # 144

The index of the target DoF position of the hands (Action):

self.cur_targets[:, self.actuated_dof_indices] = scale(self.actions[:, :16],
                                                        self.allegro_hand_dof_lower_limits[self.actuated_dof_indices], self.allegro_hand_dof_upper_limits[self.actuated_dof_indices])
self.cur_targets[:, self.actuated_dof_indices] = self.act_moving_average * self.cur_targets[:,
                                                                                            self.actuated_dof_indices] + (1.0 - self.act_moving_average) * self.prev_targets[:, self.actuated_dof_indices]
self.cur_targets[:, self.actuated_dof_indices] = tensor_clamp(self.cur_targets[:, self.actuated_dof_indices],
                                                                self.allegro_hand_dof_lower_limits[self.actuated_dof_indices], self.allegro_hand_dof_upper_limits[self.actuated_dof_indices])

self.cur_targets[:, self.actuated_dof_indices + 16] = scale(self.actions[:, 16:32],
                                                        self.allegro_hand_dof_lower_limits[self.actuated_dof_indices], self.allegro_hand_dof_upper_limits[self.actuated_dof_indices])
self.cur_targets[:, self.actuated_dof_indices + 16] = self.act_moving_average * self.cur_targets[:,
                                                                                            self.actuated_dof_indices + 16] + (1.0 - self.act_moving_average) * self.prev_targets[:, self.actuated_dof_indices]
self.cur_targets[:, self.actuated_dof_indices + 16] = tensor_clamp(self.cur_targets[:, self.actuated_dof_indices + 16],
                                                                self.allegro_hand_dof_lower_limits[self.actuated_dof_indices], self.allegro_hand_dof_upper_limits[self.actuated_dof_indices])

And always need to modify the initial position:

allegro_hand_start_pose = gymapi.Transform()
allegro_hand_start_pose.p = gymapi.Vec3(*get_axis_params(0.5, self.up_axis_idx))
allegro_hand_start_pose.r = gymapi.Quat().from_euler_zyx(1.571, -1.571, 0)

allegro_another_hand_start_pose = gymapi.Transform()
allegro_another_hand_start_pose.p = gymapi.Vec3(0, -0.4, 0.5)
allegro_another_hand_start_pose.r = gymapi.Quat().from_euler_zyx(-1.571, -1.571, 0)