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Support tele-op for Franka Kitchen environments ("vel" mode) #142

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Support tele-op for Franka Kitchen environments ("vel" mode) #142

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1ad7a4f
fix normalize for "vel" mode of robot
sriramsk1999 Apr 9, 2024
f256763
support teleop with "vel" mode environments
sriramsk1999 Apr 9, 2024
ccd8ede
fix qvel calculation
sriramsk1999 Apr 29, 2024
7381aa4
refactor calculation of action for vel/pos modes
sriramsk1999 May 1, 2024
32619e5
move goal site to end effector on init
sriramsk1999 May 1, 2024
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1 change: 1 addition & 0 deletions robohive/envs/multi_task/common/kitchen/franka_kitchen.xml
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Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,7 @@


<!-- Robot -->
<site name='ee_target' type='box' size='.03 .07 .04' pos='-0.4 0.4 2.1' group='1' rgba='0 1 .4 0' euler="0 3.14 3.14"/>
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<body name="chef" pos='0. 0 1.8' euler='0 0 1.57'>
<geom type='cylinder' size='.120 .90' pos='-.04 0 -0.90' class='panda_viz'/>
<include file="../../../../simhive/franka_sim/assets/chain0.xml"/>
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2 changes: 1 addition & 1 deletion robohive/robot/robot.py
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Expand Up @@ -574,7 +574,7 @@ def normalize_actions(self, controls, out_space='sim', unnormalize=False):
act_rng = (actuator['pos_range'][1]-actuator['pos_range'][0])/2.0
elif self._act_mode == "vel":
act_mid = (actuator['vel_range'][1]+actuator['vel_range'][0])/2.0
act_rng = (actuator['vel_range'][1]-actuator['pos_range'][0])/2.0
act_rng = (actuator['vel_range'][1]-actuator['vel_range'][0])/2.0
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else:
raise TypeError("Unknown act mode: {}".format(self._act_mode))

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28 changes: 21 additions & 7 deletions robohive/tutorials/ee_teleop.py
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Expand Up @@ -9,6 +9,7 @@
- NOTE: Tutorial is written for franka arm and robotiq gripper. This demo is a tutorial, not a generic functionality for any any environment
EXAMPLE:\n
- python tutorials/ee_teleop.py -e rpFrankaRobotiqData-v0\n
- python tutorials/ee_teleop.py -e FK1_LightOnFixed-v4\n
"""
# TODO: (1) Enforce pos/rot/grip limits (b) move gripper to delta commands

Expand Down Expand Up @@ -251,12 +252,25 @@ def main(env_name, env_args, reset_noise, action_noise, input_device, output, ho
if ik_result.success==False:
print(f"IK(t:{i_step}):: Status:{ik_result.success}, total steps:{ik_result.steps}, err_norm:{ik_result.err_norm}")
else:
act[:7] = ik_result.qpos[:7]
act[7:] = gripper_state
if action_noise:
act = act + env.env.np_random.uniform(high=action_noise, low=-action_noise, size=len(act)).astype(act.dtype)
if env.normalize_act:
act = env.env.robot.normalize_actions(act)
if env.env.robot._act_mode == "pos":
act[:7] = ik_result.qpos[:7]
act[7:] = gripper_state
if action_noise:
act = act + env.env.np_random.uniform(high=action_noise, low=-action_noise, size=len(act)).astype(act.dtype)
if env.normalize_act:
act = env.env.robot.normalize_actions(act)
elif env.env.robot._act_mode == "vel":
curr_qpos = env.sim.get_state()['qpos'][:7]
target_qpos = ik_result.qpos[:7]
qvel = (target_qpos - curr_qpos)
act[:7] = qvel
act[7:] = gripper_state
if action_noise:
act = act + env.env.np_random.uniform(high=action_noise, low=-action_noise, size=len(act)).astype(act.dtype)
# if env.normalize_act:
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# act = env.env.robot.normalize_actions(act)
else:
raise TypeError("Unknown act mode: {}".format(env.env.robot._act_mode))

# nan actions for last log entry
act = np.nan*np.ones(env.action_space.shape) if i_step == horizon else act
Expand Down Expand Up @@ -294,4 +308,4 @@ def main(env_name, env_args, reset_noise, action_noise, input_device, output, ho


if __name__ == '__main__':
main()
main()