ft[Extension]: Adds additional way of updating obstacle root_state

mppiisaac/planner/isaacgym_wrapper.py

@@ -4,6 +4,8 @@
 import torch
 import numpy as np
 
+import pathlib
+file_path = pathlib.Path(__file__).parent.resolve()
 
 @dataclass
 class IsaacGymConfig(object):
@@ -80,7 +82,7 @@ def start_sim(self):
         print(asset_file)
         self._robot_asset = self.load_robot_asset_from_urdf(
             asset_file=asset_file,
-            asset_root="../assets",
+            asset_root=f"{file_path}/../../assets",
             fix_base_link=self._fix_base,
             flip_visual_attachments=self._flip_visual,
         )
@@ -203,6 +205,7 @@ def add_box(
         pos: gymapi.Vec3,
         color: gymapi.Vec3,
         fixed: bool = True,
+        mass: float = 1.0
     ) -> int:
         asset_options_objects = gymapi.AssetOptions()
         asset_options_objects.fix_base_link = fixed
@@ -226,6 +229,9 @@ def add_box(
         self.gym.set_rigid_body_color(
             env, handle, 0, gymapi.MESH_VISUAL_AND_COLLISION, color
         )
+        props = self.gym.get_actor_rigid_body_properties(env, handle)
+        props[0].mass = mass
+        self.gym.set_actor_rigid_body_properties(env, handle, props)
         return handle
 
     def add_sphere(
@@ -272,7 +278,7 @@ def add_ground_plane(self):
     def load_robot_asset_from_urdf(
         self,
         asset_file,
-        asset_root="../assets",
+        asset_root=f"{file_path}/../../assets",
         fix_base_link=False,
         flip_visual_attachments=False,
     ):
@@ -355,7 +361,7 @@ def update_root_state_tensor_by_obstacles(self, obstacles):
             )
 
             # Note: reset simulator if size changed, because this cannot be done at runtime.
-            if not all(o_size == self.env_cfg[obst_idx]["size"]):
+            if not all([a == b for a, b in zip(o_size, self.env_cfg[obst_idx]["size"]) ]):
                 env_cfg_changed = True
                 self.env_cfg[obst_idx]["size"] = o_size
 
@@ -370,3 +376,12 @@ def update_root_state_tensor_by_obstacles(self, obstacles):
         self.gym.set_actor_root_state_tensor(
             self.sim, gymtorch.unwrap_tensor(self.root_state)
         )
+
+    def update_root_state_tensor_by_obstacles_tensor(self, obst_tensor):
+        for i, o_tensor in enumerate(obst_tensor):
+            self.root_state[:, i+3] = o_tensor.repeat(self.num_envs, 1)
+
+        self.gym.set_actor_root_state_tensor(
+            self.sim, gymtorch.unwrap_tensor(self.root_state)
+        )
+

mppiisaac/planner/mppi.py

@@ -68,7 +68,7 @@ class MPPIConfig(object):
     u_min: float = -1.0
     u_max: float = 1.0
     u_init: float = 0.0
-    U_init: Optional[List[float]] = None
+    U_init: Optional[List[List[float]]] = None
     u_scale: float = 1
     u_per_command: int = 1
     rollout_var_discount: float = 0.95

mppiisaac/planner/mppi_isaac.py

@@ -64,13 +64,9 @@ def running_cost(self, _):
         # Note: again normally mppi passes the state as a parameter in the running cost call, but using isaacgym the state is already saved and accesible in the simulator itself, so we ignore it and pass a handle to the simulator.
         return self.objective.compute_cost(self.sim)
 
-    def compute_action(self, q, qdot, obst=None):
+    def compute_action(self, q, qdot, obst=None, obst_tensor=None):
         self.sim.reset_root_state()
 
-        if obst:
-            # NOTE: for now this updates based on id in the list of obstacles
-            self.sim.update_root_state_tensor_by_obstacles(obst)
-
         # Deal with non fixed base link robots, that we need to reset the root_state position / velocity of.
         # Currently only differential drive bases are non fixed. We also have to deal with the kinematic transforms.
         if self.cfg.differential_drive:
@@ -107,6 +103,14 @@ def compute_action(self, q, qdot, obst=None):
         state = state.repeat(self.sim.num_envs, 1)
         self.sim.set_dof_state_tensor(state)
 
+        # NOTE: There are two different ways of updating obstacle root_states
+        # Both update based on id in the list of obstacles 
+        if obst:
+            self.sim.update_root_state_tensor_by_obstacles(obst)
+
+        if obst_tensor:
+            self.sim.update_root_state_tensor_by_obstacles_tensor(obst_tensor)
+
         self.sim.save_root_state()
         actions = self.mppi.command(self.state_place_holder).cpu()
         return actions